Question:
You are given the full text of a scientific research paper.

    Summarize the **research methods** used in the study in a way that is informative for policy audiences. Your response should:
    - Describe the overall study design, data sources, tools, and models used
    - Avoid unnecessary technical details or equations
    - Use accessible but precise language

    Write a short paragraph suitable for the “Scientific Research Study Methods” section of a policy brief.

    Scientific Research Paper:
    https://doi.org/10.1177/00221465211003232
Journal of Health and Social Behavior
2021, Vol. 62(2) 120–135
© American Sociological Association 2021
DOI: 10.1177/00221465211003232
jhsb.sagepub.com
Article
Researchers have established the presence of bias in
American health care with considerable work focus-
ing on provider bias within the clinical encounter.
Indeed, medical providers demonstrate bias on par
with the general population, even among those who
self-identify as holding nondiscriminatory beliefs
(Burgess et al. 2007; van Ryn and Fu 2003; Zestcott,
Blair, and Stone 2016). Understanding such bias is
important because it contributes to the profound
health disparities seen in the United States. For
instance, providers counsel and treat patients differ-
ently depending on patient characteristics, which
often disproportionately affects marginalized popu-
lations, who consistently experience worse health
outcomes (see Bao, Fox, and Escarce 2007;
Schulman et  al. 1999; van Ryn et  al. 2006). Yet
beyond recognizing the presence of bias in provid-
ers’ patient counseling and decision-making, mini-
mal research has explored how providers then
navigate bias in their care to minimize its effects. By
exploring this process, we demonstrate the ways that
various provider approaches to reduce bias reflect
and reproduce this inequality in care. Elucidating
these coping strategies simultaneously reveals not
only providers’ individually held biases but also the
social forces that engender such discrimination and
disparities within their clinical encounters.
To combat bias, researchers have recommended
numerous interventions, such as patient-centered
care, cultural competency training, organizational
reform, and diversifying medical schools and
1003232 HSBXXX10.1177/00221465211003232Journal of Health and Social BehaviorManzer and Bell
research-article2021
1University of Delaware, Newark, DE, USA
Corresponding Author:
Jamie L. Manzer, Department of Sociology and Criminal
Justice, University of Delaware, 325 Smith Hall,
Newark, DE, 19716, USA.
Email: jmanzer@udel.edu
“We’re a Little Biased”:
Medicine and the Management
of Bias through the Case of
Contraception
Jamie L. Manzer1
 and Ann V. Bell1
Abstract
There is a wealth of literature demonstrating the presence of bias throughout the American health care
system. Despite acknowledging such presence, however, little is known about how bias functions within
medical encounters, particularly how providers grapple with bias in their patient counseling and decision-
making. We explore such processes through the case of contraceptive counseling, a highly raced, classed,
and gendered context. In-depth interviews with 51 health care providers reveal that providers use four
primary strategies to navigate and minimize bias in their care—using scientific rationale, employing “safe”
biases, standardizing counseling, and implementing patient-centered care. Paradoxically, using these
strategies can exacerbate rather than resolve bias. Understanding these bias management strategies reveals
provider-held biases, how they manifest within appointments, and the potential consequences for patients’
health autonomy. Such knowledge informs interventions that promote contraceptive use among women in
the United States, addresses bias in health care broadly, and thus ultimately helps combat health disparities.
Keywords
bias, clinicians, contraception, health care, reproduction

Manzer and Bell
121
reforming their curriculum (Beach et  al. 2005;
Betancourt et al. 2003; Higgins, Kramer, and Ryder
2016; Metzl and Hansen 2014). However, few, if
any, are evidence based, and most address provider-
held biases while neglecting the structural nature of
the inequality that informs such exchanges (Metzl
and Hansen 2014; Zestcott et al. 2016). Moreover,
because little is known about how providers man-
age this bias within their patient interactions, rec-
ommendations for improving these encounters may
inevitably fall short. Indeed, the patient–provider
relationship is central to the provision of health
care. Patients are, overall, more active consumers
than in the past, and the provider–patient relation-
ship has become less paternalistic as a result.
However, because of how U.S. health care is struc-
tured and the rise of disease nationwide, the clinical
encounter is pivotal to health outcomes because
providers guide downstream access to care (Boyer
and Lutfey 2010). As such, the quality of these
exchanges must be continuously assessed.
To do so, we explore how providers navigate
and minimize bias in health care through the case of
contraceptive counseling. As scholars emphasize,
providers’ contraceptive decision-making is an
important area in which to study bias because these
encounters involve the consideration of medical,
cultural, and social factors, such as patients’ sexual
behaviors, partner histories, and pregnancy desires
(see Dehlendorf et al. 2010; Stevens 2015). Moral
judgments and cultural norms around these issues,
whether implicit or explicit, can influence provid-
ers’ prescribing patterns and the substance of their
counseling (Metzl and Hansen 2014). A growing
body of research has identified possible bias in
these areas (Borrero et al. 2009; Dehlendorf et al.
2010; Gomez and Wapman 2017; Yee and Simon
2011). For instance, some clinicians are more likely
to encourage women of color as well as young or
poor women to choose sterilization or long-acting
reversible contraception (LARC; Borrero et  al.
2009; Dehlendorf et al. 2010; Gomez, Fuentes, and
Allina 2014; Gomez and Wapman 2017; Yee and
Simon 2011). Patients are attuned to this bias,
sometimes feeling implicit pressure to use methods
they do not desire (Downing, LaVeist, and Bullock
2007; Gomez and Wapman 2017). Beyond limiting
access to women’s preferred contraceptives, such
counseling practices can cause women to desist
from contraceptive use, creating gaps in coverage,
or result in some women forgoing follow-up care
altogether, jeopardizing their overall health (Gomez
and Wapman 2017).
Additionally, the range of contraceptive methods
available and the consideration of patient prefer-
ences make it a particularly complex area of medi-
cine. Consequently, providers’ biases may be more
influential within contraceptive counseling contexts
relative to other areas of medicine where broad clin-
ical consensus for appropriate treatment leaves less
room for subjective decision-making (Dehlendorf
et  al. 2010). Therefore, to better understand how
bias functions within contraceptive counseling, we
ask: How do providers rationalize, cope with, and
manage bias within their patient interactions and
decision-making processes? Using a sociological
lens to answer such a question allows us to see not
only the existence of bias among providers but also
how it manifests and persists. Doing so highlights
the relevant structural features of medicine along
with the broader social forces that produce bias in
health care. Understanding this link offers opportu-
nities for multilevel solutions to reduce bias in med-
icine and the disparities it perpetuates.
Background
Health Care and Bias
Researchers have extensively documented health
disparities in the United States, noting that relative
to more privileged patients, marginalized popula-
tions experience worse health outcomes and have
less access to quality health care (Adler and
Newman 2002; McLeod 2015; Smedley, Stith, and
Nelson 2002). Variables such as race, class, gender,
and ethnicity become factors in care because of the
provider–patient interaction as well as the structural
contexts in which these interactions occur. Like
with other institutions, medical organizations are
not value neutral. Providers apply cultural knowl-
edge and stereotypes in these professional spaces
just as they do in lay settings to make, often uncon-
scious, moral evaluations about patients as they
diagnose and treat them (Spencer and Grace 2016).
Researchers note that patient characteristics
consistently predict providers’ decisions even when
access to and use of medical care and presenting
symptoms are controlled (see Metzl and Hansen
2014; Spencer and Grace 2016). Some of this varia-
tion in care is related to provider bias that persists
despite substantial efforts within medicine to
address both these disparities and the bias that con-
tributes to them (Chapman, Kaatz, and Carnes
2013).1 Addressing provider bias is particularly
challenging because racism and other forms of dis-
crimination and privilege now operate in less overt

122
Journal of Health and Social Behavior 62(2)
ways within the clinical encounter. For instance, as
racism and the effects of racialized structures
become less explicit, it is easier for health care pro-
viders and lay people to deny racial stratification in
the United States as well as individually held racial
bias (Bonilla-Silva 2010). Further complicating
efforts to reduce bias, providers can simultaneously
hold explicit egalitarian beliefs about patient groups
but still harbor implicit forms of bias that interfere
with their interactions, affect, and evaluation of
patients (Chapman et al. 2013). Therefore, under-
standing how providers manage bias within their
care is a key step in identifying both how bias per-
sists and how to address it.
Increasingly, scholars note that although pro-
vider bias is a significant factor in predicting dispa-
rate care, more needs to be done to understand the
mechanisms that underlie these patterns. For
instance, Metzl and Hansen (2014) critiqued cul-
tural competency training, a popular intervention
used nationwide to combat bias by encouraging
providers to be reflexive about their personal
worldview and develop competence toward patients
with diverse backgrounds. Instead, they proposed
structural competency training, noting that cultur-
ally sensitive clinicians do little to mitigate the
effects of the structural barriers patients experience
when accessing care.
Conversely, cultural competency training and
other similar interventions may, instead, prompt
providers to assume these structural barriers, such
as challenges accessing medication, reliable trans-
portation, and lack of healthy foods, to be the result
of patients’ cultural preferences that can imply
either a moral failing or moral superiority (Metzl
and Hansen 2014). In other words, structural-level
factors inform clinical interactions, diagnosis, and
treatment when subsequent assumptions about cul-
tural groups become embedded in providers’ lan-
guage and beliefs to the benefit of some and harm
to others (Metzl and Hansen 2014). Exploring how
providers manage and attempt to minimize bias
reveals how this language and entrenched beliefs
affect providers’ actions within medical contexts
and subsequently, patients’ experiences, outcomes,
and autonomy.
In this way, provider interactions and decision-
making have multiple spheres of influence beyond
strictly physical manifestations of illness that need
to be better understood (see Spencer and Grace
2016). For instance, providers triage, diagnose, and
treat patients in emergency rooms differently based
on a range of patient characteristics (e.g., cleanli-
ness, insurance status, and perceived morality) that
have little to do with their presenting symptoms
(Lara-Millán 2014). This occurs because institu-
tions like medicine produce and reproduce cultural
values that result in disparate care by creating a
“commonsense” medicine where stereotypes about
social groups serve as explanations for their cir-
cumstances that, over time, gain medical legitimacy
(Bonilla-Silva and Baiocchi 2001; Metzl and
Hansen 2014). Such findings are particularly poi-
gnant for reproductive health care given the United
States’s continued use of contraception to encour-
age the births of more privileged women over those
of marginalized women (Colen 1986; Gomez et al.
2014; Roberts 1997).
Indeed, medicine is a racialized system where
one’s health status is predetermined by and predi-
cated on other racialized structures, such as educa-
tion, economic policy, and the law (Bonilla-Silva
and Baiocchi 2001). These structures are also gen-
dered and classed in ways that are often invisible to
providers. This inequality has been found to limit
access to resources and health care, ultimately
affecting what needs patients present with and how
providers then perceive these patients (see Spencer
and Grace 2016). Importantly, institutionalized
norms and practices of predominantly white,
Western nations manifest in these structures, where
assumptions and stigma about individual patients
are not produced in individual encounters but are,
instead, enacted and normalized within these
exchanges (Carmichael 2003). Indeed, how provid-
ers manage bias within their care helps to reveal
these often insidious biases and elucidates the
social forces that engender them within patient–
provider interactions. Doing so supports necessary,
multilevel interventions to address both provider
biases and broader health care inequity.
Contraceptive Counseling and Bias
Social justice efforts such as the reproductive justice
(RJ) movement, begun by a group of black women
in the mid-1990s, emphasize this nexus between
intersecting social identities and the effects of struc-
tural inequalities on health—specifically, women’s
right to have children (or not) and to parent their
children in a safe and healthy environment (Luna
and Luker 2013). A key tenet of the RJ movement is
identifying contraceptive equity as a human right.
Through this RJ lens, women’s contraceptive
choices should be fully informed and fully volun-
tary, and these methods should be readily available
(Gomez et al. 2014; Gubrium et al. 2016; Roberts
1997). Women should be able to change or remove

Manzer and Bell
123
methods when they desire without undue provider
influence as well as feel respected when they elect
not to use contraception (Higgins et  al. 2016).
However, provider bias and the structural inequality
that shapes these exchanges have been found to
undermine the quality of marginalized women’s
care and their contraceptive decision-making, which
have broad implications for how women manage
their fertility (Gomez et  al. 2014; Gomez and
Wapman 2017; Gubrium et al. 2016; Higgins et al.
2016).
Indeed, as RJ advocates and scholars empha-
size, contraception has historically been used as a
mechanism to limit the births of socially marginal-
ized women in the United States through intentional
involuntary sterilization and eugenics programs
(Dehlendorf et al. 2018; Gomez et al. 2014; Gomez
and Wapman 2017; Gubrium et al. 2016; Higgins
et al. 2016). Some researchers caution that provid-
ers may, often inadvertently, carry on these tradi-
tions by persuasively counseling or implicitly
pressuring marginalized women to use contracep-
tion more so than their privileged patients—
particularly LARC methods, such as the intrauter-
ine device (IUD) or subdermal arm implant
(Borrero et al. 2009; Dehlendorf et al. 2010; Gomez
and Wapman 2017; Gubrium et al. 2016; Yee and
Simon 2011). Moreover, providers may be more
hesitant to remove a LARC when requested by a
woman of lower socioeconomic status (SES) or
woman of color, citing perceptions of pregnancy
risk related to patient age, gender, perceived sexual
promiscuity, and readiness to parent (Gomez et al.
2014; Henderson et al. 2011; Kalmuss et al. 1996;
Littlejohn and Kimport 2017; Stevens 2015).
Borrero and colleagues (2009) noted that this
unevenness in contraceptive counseling is not
patient initiated; rather, unconscious beliefs about
who should reproduce may affect providers’ care.
Importantly, providers recognize their influen-
tial position (Henderson et  al. 2011). Henderson
and colleagues (2011) found that most clinicians
believed they could exert influence on their
patients’ contraceptive choices through how they
presented contraceptive information to them.
Clinicians described emphasizing different benefits
and side effects of methods to influence patient
choice and made efforts to make an emotional
impression that might motivate behavior change.
Providers also attempted to use fear and shame to
encourage women to adopt healthy sexual behav-
iors. Given that providers recognize their influence
within appointments and that biased care is an
ongoing issue in U.S. health care, there is a need to
better understand how providers cope with such
bias within their appointments and its potential
effects on patients’ well-being.
Data and Methods
To gain such an understanding, we drew on 51 semi-
structured interviews with health care providers.
This research was part of a larger evaluation of a
statewide initiative focused on improving access to
and delivery of contraceptives, particularly LARCs.
We recruited provider participants through
phone, email, and letters to their workplace. Twenty-
three (45%) were physicians, 19 (37%) were nurse
practitioners, 8 (16%) were nurse midwives, and 1
(2%) participant was a nurse. These providers spe-
cialized in pediatrics, family medicine, obstetrics
care, gynecological care, and obstetrics/gynecology.
Forty (78%) participants were from private clinic
sites, and 11 (22%) were from Title X-serving clinic
sites. The average age of respondents was 46 years
old, ranging from 26 to 74. On average, providers
practiced for 16 years, with a range between 2 and
47 years in practice. Thirty-eight (74%) providers
identified as white/white non-Hispanic, 9 (18%) as
African American or black, 3 as Indian/Asian (6%),
and 1 (2%) as Hispanic. Our sample was relatively
racially diverse compared to the U.S. physician pop-
ulation, where only 6% of physicians are black
(Xierali and Nivet 2018).
Most interviews were held in private offices at
the clinic sites, and participants provided informed
consent prior to interview initiation. Interviews
lasted, on average, 71 minutes, with a range
between 38 minutes and nearly 2 hours. The inter-
view inquired about providers’ history practicing
medicine, their contraceptive prescribing patterns,
and details about how they engage in dialogue with
their patients about contraception. At the comple-
tion of the interviews, the providers received $40
grocery store gift cards.
A diverse research team of five conducted and
analyzed the interviews, collaborating to construct a
rigorous analytical process. Codes were developed
inductively, similar to a grounded theory approach,
through an iterative process of reading transcripts to
identify patterns and themes, many of which cen-
tered around questions that we posed to participants,
such as if and how they counsel patients from differ-
ent backgrounds about contraception (Charmaz
2014). We then entered codes such as “specific con-
traceptive for specific patient” into a qualitative
research software program and further refined them
into hierarchical coding schemes using line-by-line

124
Journal of Health and Social Behavior 62(2)
coding. Given the semistructured nature of the inter-
views, findings for this article around providers’
efforts to minimize and manage bias emerged
unprompted during the interviews when providers
were asked about their approaches to patients. We
then ensured that all examples fit the codes and
subcodes. To maintain consistency, researchers
reviewed transcripts in pairs for intercoder reliabil-
ity and continuously memoed throughout the coding
process about relevant themes. All quotes used in
this article were the most illustrative examples
among many of the participants. Data were only
included that were commonplace across partici-
pants. This research was reviewed and approved by
state and university Institutional Review Boards. All
names are pseudonyms.
Results
As prior research has found, provider bias continues
to be a poignant issue in contemporary American
medicine and within contraceptive counseling (i.e.,
Borrero et al. 2009; Dehlendorf et al. 2010; Gomez
and Wapman 2017; Gubrium et al. 2016; Yee and
Simon 2011). For instance, Michelle, an African
American nurse practitioner, reflects, “You have a
Hispanic population who they pretty much pop kids
out left and right.” In addition to ethnicity, providers
frequently distinguish patients based on their SES.
Prya, an Indian physician, explains:
I mean we have patients who have been popping
babies out every year–year and a half and . . . don’t
want birth control [yet] are the ones from a lower
socioeconomic status with much lesser education
where they are not thinking about, “Hey, I have
to take care of this child.” . . . They don’t care if
the child finishes school.
To better understand what drives this persistent
bias, how it manifests within the clinical encounter,
and its potential consequences for women’s repro-
ductive autonomy, we explore how providers nego-
tiate and manage bias within their contraceptive
decision-making and counseling. In doing so, we
observe four primary strategies providers imple-
ment to justify, rationalize, minimize, and/or deny
bias: first, couching their bias within scientific ratio-
nale; second, employing “safe” biases to minimize
differential counseling; third, standardizing their
approaches to patients as a means of denying differ-
ential care; and finally, using the tenets of patient-
led care to distance themselves from responsibility
for patients’ final contraceptive decisions.
Couching Assumptions in Scientific
Rationale
Providers frequently evade claims of bias in their
care by utilizing data, especially around pregnancy
risk, to rationalize their differential contraceptive
counseling. These data function to produce and
reinforce biases about patients that then inform pro-
vider practices. Scientific arguments have long been
used to encourage and validate eugenicist beliefs
about who should reproduce, especially regarding
women of color (Roberts 1997). In its more modern
and implicit form, such claims hinge on references
to aggregated data about contraceptive efficacy and
women most at risk for unintended pregnancies.
These data prompt providers to anticipate certain
patient risks and contraceptive needs. Kathleen, a
white nurse midwife, explains:
Young women I am more likely to dissuade from
using pills [b]ecause of research. . . . Like I have
heard anything from 84 to 89% [efficacy] . . . I
mean I have been here for a long time and at
times it really feels Third World and (pauses) just
it seems like especially with teenagers . . . where I
worked before, that [pregnancy] was almost
celebrated. It wasn’t frowned upon or necessarily
considered (pauses) bad.
Kathleen cites statistical evidence of the pill’s effi-
cacy among teenagers to express why she discour-
ages pill use among these patients. However, she
follows that evidence with subjective, value-laden
assertions about teen pregnancy being implicitly
“Third World” and “bad,” thus requiring teens to be
counseled against using less effective methods. In
rooting this decision-making in scientific rationale
vis-à-vis the data she cites, Kathleen overlooks and
justifies her persuasive counseling approach without
acknowledging its inherent bias around pregnancy
intendedness among teenagers that may also moti-
vate her decision-making. Furthermore, Kathleen’s
reference to the “Third World” also speaks to class-
and race-based assumptions about “bad” nonnorma-
tive pregnancies being antithetical to Western, “First
World” childbearing expectations.
Public health data that identify some women as
most “at risk” for unplanned pregnancies prime
providers to preemptively conclude which contra-
ceptives may benefit specific patients. Robert, a
white nurse practitioner, loosely references these
data on pregnancy risk and contraceptive compli-
ance to explain why he is more likely to steer young
patients of low SES toward LARCs. He remarks:

Manzer and Bell
125
[M]y appointment would say “Title X” and then
“birth control” would be the thing that’s after
it . . . and then I would steer them. . . . [W]e’re a
little biased towards trying to get one of
the . . . LARCs in . . . our younger Title Xs just
because . . . they are statistically at a higher risk
for becoming pregnant . . . or for not understanding
and following through on their birth control.
Robert conducts a form of “social triaging,” a practice
common across participants, where he draws preemp-
tive conclusions about patient risk and contraceptive
appropriateness based on a patient’s demographic
traits relative to national trends on pregnancy risk and
contraceptive use. These data become institutional-
ized knowledge or a “commonsense” way of evaluat-
ing patients from specific backgrounds.
In this way, Robert appears to use what he
believes to be data-driven decision-making about
who is “statistically” most at risk for unintended
pregnancy as rationale to “steer” some patients to
use LARC methods rather than prioritizing his
patients’ preferences. By justifying his counseling
approach using statistics, Robert validates his dif-
ferential counseling without having to confront the
inequity of this approach that could limit some
women’s pregnancies. In doing so, Robert does not
question why some patients may be less compliant
or knowledgeable about contraception, neglecting
profound structural factors that may be causing
these differences. Moreover, Robert’s comments
have an implicitly racial undertone. Title X family
planning clinics disproportionately serve women of
color, suggesting that such steering for his “Title
Xs” may disproportionately affect racial minorities.
In these ways, interpersonal interactions within the
clinical encounter can reflect and produce systemic,
gendered racism around reproductive equity and
contraceptive access.
Whereas Robert loosely cites statistical evi-
dence that some women are at greater risk of unin-
tended pregnancy, most providers first acknowledge
unintended pregnancy can happen to anyone and
thus remark that all reproductive-age women bene-
fit from contraceptives. However, turning to demo-
graphic information that differentiates pregnancy
risk levels across patient populations allows provid-
ers like Peter, a white physician, to justify some
women’s greater need for contraception. He states:
I think unintended pregnancy breaks all socio
economic barriers, but it probably is more
prevalent in the more populated, inner city,
lower SES neighborhoods than it is in [names
two
wealthy,
largely
white
suburban
neighborhoods] . . . I think people who are in
families where career and education are valued
very high, I think there is a lot more education
about
unintended
pregnancy
and
sexual
promiscuity. . . . It doesn’t mean [unintended
pregnancy] doesn’t happen. It just means it’s
probably less typical.
On the one hand, Peter acknowledges that unin-
tended pregnancy affects women from all back-
grounds but infers that unintended pregnancies are
nonnormative and more prevalent among poor
urban women whose family values around educa-
tion, career, and sexual behaviors are implicitly less
socially acceptable. Peter’s sense of who is more
likely to experience an unplanned pregnancy is
founded in cultural assessments of patient behavior
that his use of data-oriented terms like prevalence
allows him to mask. Importantly, Peter’s experience
shows just how cultural competency, in this case,
the ability to acknowledge that some families value
neoliberal goals of wealth and career while others
may not, can reinforce stigma toward patients while
neglecting the structural limitations that create gaps
in social, economic, and health outcomes across
populations (Metzl and Hansen 2014).
When asked how to reduce unintended preg-
nancy rates, Peter reemphasizes the differences
among populations affected by unintended preg-
nancy and who should ostensibly be targeted for
interventions that encourage LARC methods,
saying:
Put contraception in the drinking water, put it
into the ventilation systems. . . . If that’s not
considered ethically appropriate, then [do]
something like making it a statewide [LARC]
initiative . . . that’s based on the literature and the
research they’ve done. If you knew that 100% of
unintended pregnancies took place in [these two
low SES, urban zip codes], then yes, I would say
target [those two zip codes]. . . . But it doesn’t
mean we shouldn’t be educating everybody . . . but
the way you deliver that message may be
different. The way you sell alcohol to inner cities
and the way you market and sell alcohol to the
rich is different. It’s about what attracts their
attention.
Peter suggests that everyone benefits from informa-
tion about LARCs, but in this case, by citing evi-
dence of pregnancy risk from the medical literature,
he can ignore his use of coded assertions about for

126
Journal of Health and Social Behavior 62(2)
whom and when pregnancy is socially acceptable.
Peter also reflects rhetoric common in LARC pro-
motion efforts that encourage providers to empha-
size the most effective methods based on presumed
pregnancy risk rather than patient preferences or the
patients’ personal risk of side effects related to con-
traceptive use (Bertotti, Mann, and Miner 2020;
Gomez et al. 2014; Littlejohn 2013; Littlejohn and
Kimport 2017).
Interestingly, Peter likens medical advice to sell-
ing a product such as alcohol. Ironically, reflecting
research (e.g., Moore, Williams, and Qualls 1996)
regarding alcohol propaganda, these marketing
strategies are not only rooted in disparate stereo-
types, but they also further perpetuate such images
and inequalities. Placing his reasoning for the vari-
ous emphases in evidence, however, is a mecha-
nism that Peter uses to support and justify his biased
communication and advice. Similarly, although
Peter’s reference to putting contraception in the
drinking water or ventilation system is likely face-
tious, such comments reflect the near universal
appeal some providers see in limiting unintended
births among marginalized women without suffi-
cient recognition of how such measures could
undermine their patients’ reproductive autonomy.
Indeed, although explicit eugenicist policies have
been abandoned, the logic that fostered them can
unconsciously persist in some providers’ attitudes
and subsequent counseling about which contracep-
tive methods are most appropriate for which patient
population (Luna and Luker 2013).
For instance, to limit the number of unintended
pregnancies, providers like Mark, a white physi-
cian, do not explicitly tell their marginalized
patients not to get pregnant; rather, they more
strongly encourage LARC use among those popula-
tions. Mark explains:
The other thing that really frustrates the crap out of
me—excuse me—is the patient who comes in and
says, “No, I don’t want to be pregnant, but I don’t
use any birth control” . . . you want to take that
person and shake them. Some of it is ignorance,
some of it is cultural . . . I’m not telling you not to
get pregnant. I’m telling you, “If you don’t want to
be pregnant, you should do something to prevent
it” . . . I hate to say this, but I think it’s a fact that
there is a strong correlation between African
American and low socioeconomic status . . . I think
the white population seems to be overall more
likely to be knowledgeable, compliant, etc. And
it’s not meant to be a racist statement (laughs).
Clearly, Mark is frustrated when patients, particu-
larly black women of low SES, state that they do not
want children yet do not use contraception to pre-
vent such a pregnancy. The emotion evident in
Mark’s quote alongside its content reveals the
stigma that he holds about black low-SES women
and runs counter to traditional views of medical pro-
fessionals as unemotional, objective, and value neu-
tral. In effect, Mark debates whether to blame the
individual women and their cultural preferences for
their inconsistent contraceptive use or recognize
that rather than being a moral failing, their “igno-
rance” about birth control is rooted in structural
inequality around education and health care access.
In situating these women’s attributes in statistics
(i.e., “correlation”), Mark applies race and class
assumptions without the implication of being
biased. Indeed, he explicitly rejects the notion that
he made a “racist statement.” As Metzl and Hansen
(2014) cautioned, teaching providers to be cultur-
ally competent may promote stereotypes about
whole demographic groups when providers fail to
link structural barriers faced by these populations to
their health outcomes and decision-making.
Differentiating Patient Care Using
Safe Biases
In addition to couching bias within scientific ratio-
nale, providers employ what we term safe biases as
a minimizing strategy. Safe biases are like coded
language in that both convey multiple layers of
meaning and target a specific group of people to
whom the term becomes associated, such as thug or
criminal. In this context, safe biases include using
medically acceptable, often professional terms like
Medicaid, at risk, insured, or underserved to safely
express race-, class-, or gender-based beliefs about
patients and their behaviors.
For example, Kevin, a white physician, uses
safe biases to implicitly link pregnancy risk to
women of low SES. He explains, “Education is
hugely important. You need to care for yourself
before you can care for somebody else. . . . They’re
just completely not educated [about birth con-
trol] . . . neighborhoods where four generations and
three generations have been on public assistance.”
Kevin reflects common, though contested, asser-
tions that unintended pregnancies more frequently
occur among less educated women, have intergen-
erational effects, and bear social costs related to
inevitable welfare expenditures—a frequent justifi-
cation for LARC interventions (see Gubrium et al.

Manzer and Bell
127
2016; Thomas and Monea 2011). Kevin’s use of
“public assistance” has negative connotations that
are classed and raced. The phrase is often code for
low-income communities and communities of color
presumed deficient or unproductive (Gold 2014). In
this case, Kevin associates public assistance with a
need for contraceptive education, explicitly stating
that these women cannot care for themselves or
their children. By neglecting the structural inequali-
ties that force families onto welfare rolls, Kevin
invokes political and public health discourses that
justify state-sponsored efforts to limit reproduction
through contraception and welfare reform (Gold
2014). By using safe biases like uneducated and
public assistance, Kevin can openly discuss the
merits of limiting the births of welfare recipients
without taking responsibility for the prejudicial
thinking that undergirds such beliefs. His statement
also demonstrates how readily discriminatory
assumptions about patients can subtly pervade con-
traceptive counseling practices.
Stephanie, a white nurse, further elaborates on
these assumptions when differentiating patients by
insurance type—a safe and common medical refer-
ence used to distinguish patients by social class:
And we cater to a specific population because we
are private insurance only. . . . Because we don’t
have your Medicaids or your more underserved
populations [but] I did some volunteering
recently [and] the clientele are . . . people who
don’t have private insurance. . . . Well, your
underserved population. Yeah. I don’t like the
word “poor” (laughing) . . . They’re young and
typically if you’ve had one unplanned pregnancy,
you’re more likely to have another. So it’s just
kind of the way the cycle goes. . . . But if they’re in
the underserved populations, it’s not going to
help anybody to have multiple children [and]
they’re not going to buy condoms, they’re still
going to have sex. . . . And so it would make the
most sense to put themselves in the easiest
situation possible for long term where they don’t
have to worry about having a baby [by using] your
Mirenas, your ParaGards [IUD brands] . . . which
is what I would recommend.
Stephanie not only discusses SES indirectly by ref-
erencing Medicaid versus privately insured patients,
she also suggests that poor young women have
unprotected sex that can result in multiple, likely
unplanned, children. Stephanie makes a semantic
move by substituting the more loaded term poor for
safer ones like underserved and Medicaids to
describe these women (Teal et al. 2010). Yet despite
doing so, Stephanie is still more likely to prescribe
an IUD to a poor woman because shifting her lan-
guage simply obscures the bias rather than confronts
it. Using safe biases allows for this more explicit
bias to be managed, justified, and discreet in her
assertions that IUDs are ideal for women of low
SES. Moreover, recommending LARCs to help put
women in the “easiest position possible” neglects
the structural factors that put these women in such
difficult, marginalized positions in the first place.
Providers often use these safe biases to con-
struct and explain their different communication
approaches with patients. Rather than candidly dis-
cussing issues of race or class with the interviewer,
Drew, a white physician, explains these different
approaches by first using patients’ education levels
and later more explicitly tying education to SES
and culture to race:
I mean the “less educated” person . . . One . . .
they’re not exposed to the information as much . . .
I find that the less educated people . . . don’t
understand well when I try and explain
[contraception] . . . whereas somebody who’s a
higher-educated person . . . they obviously get [it].
So it makes it easier to give a scientific answer to
them whereas for the lower socioeconomic, less
educated people, it is a little harder . . . I speak in a
very different way . . . at least our African American
population is probably a lower socioeconomic
status than our average Caucasian. So it’s hard to
tell whether it’s the cultural part or the
socioeconomic part—and . . . I try not to bring my
morals into when I counsel people. But we had
single parents . . . that can’t take any more kids
and that the government is paying for
Medicaid . . . and [the kids are] not getting raised
well. . . . Sometimes people need help, but we
can’t enable them for so long that they can’t help
themselves.
Drew’s statements are demonstrative of color-blind
racism, where individuals develop verbal strategies
to conceal directly racist statements (Bonilla-Silva
2002). By virtue of using safe biases common
within the medical profession like education and
Medicaid with coded language such as cultural,
Drew attempts to avoid making directly racist,
classist, and moral statements about patients.
Paradoxically, Drew demonstrates reflexivity by
saying he does not want his personal morality to
shape his contraceptive counseling. However, he
argues that single, presumably black, parents with

128
Journal of Health and Social Behavior 62(2)
publicly funded insurance neither need additional
children nor parent their current children well. As
RJ advocates and scholars caution, providers may
prescribe contraception based on a patient’s per-
ceived ability to parent in culturally acceptable
ways—limiting the number of births to marginal-
ized women by recommending LARCs or steriliza-
tion (Higgins et  al. 2016; Roberts 1997). Such
beliefs, no matter how implicitly held, can creep
into contraceptive counseling practices that affect
the reproductive autonomy of marginalized groups
while privileging white patients of high SES.
Standardizing Contraceptive Counseling
across Patients
Medical care has become increasingly standardized
in response to nationwide reports of inconsistent
treatment and outcomes (Smedley et al. 2002). This
standardization may encourage objectivity and neu-
tralize biased clinical encounters by offering similar
contraceptive counseling to each patient. However,
as reflected through Mark’s quote, this approach
may actually promote prejudicial thinking. He says:
Then it’s an issue of again getting back to the one
key question: Do you want to have a baby in the
next year or two? . . . And there are a lot of women
who are single and who want to have a baby and
again, some of them are teenagers who am I to
say they shouldn’t, but they probably shouldn’t.
Some of them are professional women who,
“Yeah, yeah, I want to have a baby. And I don’t
care who the father is.”
Mark references an effort to reduce unintended
pregnancy, in which more than 30 U.S. states train
their providers to ask women ages 18 to 50 about
their pregnancy desires at each appointment (Power
to Decide 2019). Despite using this standard
approach, Mark’s statement reveals his personal
beliefs about the acceptability of pregnancies
among single mothers, comparing “professional”
women to teens. However, asking all women about
their pregnancy desires allows Mark to feel he is
inquiring objectively about women’s family plan-
ning despite their background.
Often it is the providers who most decisively
describe their standardized approaches who are
more likely to unconsciously encourage “at-risk”
patients to use LARCs relative to their more privi-
leged patients. This is reflected in the experiences
of Vanessa, a white physician:
But I think if you are standard in the way that you
ask everybody, it’s for me personally the best
way to try to eliminate any bias. . . . If I ask every
kid the same thing in the same hopefully sensitive
way, then I am not—I am hopefully trying to
eliminate any personal biases or perceptions . . . a
lot of medicine is trying to recognize quickly
certain things or trying to think about what your
patients are most at risk for . . . I really think almost
every kid would benefit from a LARC . . . so I
really try to recommend them . . . [they] are really
the way to go.
As evinced above, providers are sensitive to bias
and seek to manage it by standardizing patient care.
Indeed, Vanessa suggests that all patients engaging
in certain behaviors would benefit from LARCs.
However, health care constructions of “risky behav-
iors” are inherently raced and classed rather than
unbiased, as this provider suggests (Metzl and
Kirkland 2010). As other research affirms, when
contraceptive counseling efforts go beyond ensur-
ing access for all women to then promoting LARC
use among specific populations, marginalized
women are more likely to experience restricted con-
traceptive options that can serve to undermine
reproductive autonomy (Gomez et al. 2014).
Yet other providers describe their contraceptive
counseling as consistent across all patients and
actively deny race- or class-based discrimination.
Dayna, a white physician, says:
I try and give the same exact word-for-word to
everybody . . . I don’t say, “Oh, you’re black.
Let’s just talk about this.” Or, “Oh, you’re white,
we’re just going to talk about this.” . . . [A] good
portion of [my current patients] have a college
education . . . so I will speak to them a little bit
more medically . . . I don’t want to say “bigger
words”—but
I’ll
use
more
data . . . in
residency . . . that was more like an inner city
office, usually lower socioeconomic status,
maybe not high school education, a little bit
more of like an urban population . . . I don’t know
if this is maybe like a weird prejudice, but I’ll try
to use examples with like that more urban kind
of undereducated [patient]. I think I had a
19-year-old girl a few months ago who was like,
“I just don’t care if I get pregnant.” I was like,
“That’s an awful attitude.”
Despite claims to the contrary, Dayna does adjust
her counseling strategies based on patient race and

Manzer and Bell
129
class. Indeed, she uses implicitly racialized lan-
guage like urban and inner city, two terms that are
code for black, poor patients. Dayna linguistically
enacts color-blind racism both by patronizing black
patients through her differential counseling and by
referencing race indirectly when speaking about
these exchanges (Bonilla-Silva 2002). By asserting
her color-blind, standardized approach, Dayna’s
biased counseling can continue unabated without
intervention. Moreover, calling her teen patient’s
attitude about pregnancy ambivalence “awful”
reveals just how strongly some providers feel about
discouraging nonnormative pregnancies. Such sen-
timents may affect providers’ contraceptive coun-
seling and subsequently, women’s contraceptive
choice.
Facilitating Choice with Patient-
Centered Care
Patient-centered care, like standardization, is a
widely used model designed to improve health care
outcomes (Epstein and Street 2011). Patient cen-
teredness encourages patients to guide clinical
exchanges and strives for providers to support
patient decision-making with a sense of empathy
and collaboration (Epstein and Street 2011; Stewart
et al. 2000). Most providers inherently support these
goals. Nicholas, a white physician, explains, “I
always tell people, ‘I am a democracy . . . not a dicta-
torship.’” However, unacknowledged biases under-
mine patient-centered care. In fact, most providers
implement tenets of patient-centered care by advo-
cating for women’s contraceptive choice. However,
the belief that they are patient centered in their care
often prevents providers from confronting their per-
sonally held biases and recognizing how such biases
influence their counseling practices (Stewart et al.
2000). Nicholas continues saying:
If [the patient] asks me my personal preference,
I will always tell them . . . LARCs. . . . But I would
never force somebody . . . I would say . . . “Well, if
you’re letting me pick, I want you to have a
Mirena [a brand of IUD],” but I’m giving you all
the options so you can make an informed
decision about what’s going to work best for
you, and your lifestyle, and be the most
effective . . . I’m going to show you that I am
really looking out for your best interest.
Patient-centered approaches may prioritize patient-
directed contraceptive decision-making; however, in
implementation, providers’ beliefs about who should
use LARCs subvert such outcomes. Nicholas clearly
describes exerting his clinical authority over patients’
decisions by actively encouraging the use of a LARC
for women whose “lifestyles” he believes warrant
LARC use. However, Nicholas can deny unduly
influencing the contraceptive decisions of specific
patient populations by arguing that his care is patient
centered and that he is a “democracy.”
Furthermore, Nicholas’s reference to lifestyle
evokes a similar meaning as culture, especially
when racially connoted. Culture is often a proxy for
race, and lifestyle is a common proxy for risky
behaviors attributed to various racial or social
groups. Shim (2005:415) argues that these “cultural
constructions of race resonate particularly well with
health promotion and risk reduction mandates.
Viewing race through the prism of culture provides
an extremely convenient entry point for targeting
risky lifestyle behaviors,” which in this case refers
to unprotected sex and pregnancy risk. Out of con-
cern for women’s pregnancy risk, their lifestyle
choices, and best interest, Nicholas is more likely to
counsel these women to use LARCs.
Even providers who openly advocate for
patients to obtain their contraceptive choice use
persuasive counseling techniques to influence those
decisions. For instance, Beverly, a white nurse
practitioner, reflects, “I read the consultation notes
of the patients that have babies . . . to make sure that
they got their birth control of choice.” When asked
to describe her counseling approach in these cir-
cumstances, Beverly explains:
Well, if they have multiple kids at home, I really
try to get them to get their tubes tied . . . I’m like,
“Look,
nobody
likes
kids
that
much
(laughs).” . . . Or if they won’t do that, I really try
to get them to do a LARC . . . so that they don’t
have to keep running in here . . . you can just tell
the poverty. . . . You don’t need any more kids.
Beverly is adamant that her patients make contra-
ceptive decisions, a core tenet of patient-centered
care. However, Beverly is more likely to recom-
mend sterilization or LARC methods to impover-
ished women with multiple children. By claiming to
support patient choice, Beverly does not confront
how her beliefs may subsequently undermine this
choice. Through such examples, it becomes appar-
ent that efforts to be patient centered are impaired
by persistent provider bias. It is also evident that
claims of patient centeredness may skew providers’
perceptions of their own care and rather than
improve health care equity, actually serve as a

130
Journal of Health and Social Behavior 62(2)
means through which providers can deny, and thus
perpetuate, biased care.
Providers do recognize the complex factors that
can affect not only how they make prescribing deci-
sions but also if decisions can realistically be
patient centered. For instance, structural issues
within medicine can dictate what methods patients
can access and which providers can prescribe. Ruth,
a white nurse practitioner, shares:
For the most part, I mean nobody pushes any
service or anything on a person. We’re not in that
day and age nowadays. But at the same point in
time, if here are your choices: X, Y, Z. You’ve
got to choose X, Y, Z because that’s what we
have available to you. That’s the thing that’s
most easy for you to get X, Y, Z. “Oh, you want
H? Oh, then we have to do a little bit more work
on that. Hm. I have 15 minutes.” So and it all
plays a role. So when you’re looking especially
Medicaid and what Medicaid will pay for.
Ruth describes how the structure of medicine can
limit providers’ ability to enact patient-centered
contraceptive care due to time constraints, over-
scheduling, stocking issues, and insurance-related
restrictions. She also notes that patients of lower
SES who have Medicaid may be particularly
impacted by these constraints. Angela, a white phy-
sician, adds:
It’s really easy to see things in black and white
and then as soon as you start to see the gray . . . it
becomes harder to navigate the patient
encounters . . . my goal is always to assess what
the patient’s desires are . . . helping them decide
what’s right for them. That’s the ideal. Sometimes
it’s easier said than done. . . . Some of it are issues
with the patient . . . and sometimes it’s the
system . . . ideally we would have plenty of time
to go through all of the methods, whatever the
woman picked that day, we could then provide
her with. But there are lots of barriers to doing
that.
Importantly, as other researchers have noted,
stressed providers in ambiguous or “gray” situations
amid rushed interactions are more likely to rely on
stereotypes when making clinical decisions
(Burgess et  al. 2007). As such, although Angela
does not describe explicitly biased beliefs, she and
Ruth describe an environment in which such biases
thrive.
As Ruth suggests, times have indeed changed;
providers may be less likely to explicitly coerce
women to use contraceptives. However, we find
that this coercion has simply taken on a more sub-
tle, “gray” form, easily couched in scientifical ratio-
nale, made implicit using safe biases, and rationalized
through standardized or patient-centered approaches
to care.
Discussion
Recognizing how health care providers grapple with
bias offers opportunities to better understand the
biases they hold, how bias manifests within appoint-
ments, and how patients’ autonomy and outcomes
may be affected. Elucidating these management
strategies simultaneously reveals not only provid-
ers’ biases but also the social forces that engender
such discrimination and inequalities within the clin-
ical encounter.
Most providers aspire to offer equal access to all
contraceptive varieties in alignment with their
patients’ preferences. To do so, they employ a range
of strategies to navigate and minimize potential
bias that may undermine this goal—the most salient
of which we found to be couching bias within sci-
entific rationale, speaking about these risks using
safe biases that avoid the explicitly raced and
classed language, adopting standardized approaches,
and employing patient-centered care. However, we
find that providers’ efforts to provide equitable care
paradoxically perpetuate this bias.
Biased care is challenging to confront, espe-
cially, as researchers note, providers are less
explicit in their biases than in prior decades (Blair,
Steiner, and Havranek 2011; Bonilla-Silva 2002;
Zestcott et al. 2016). The insidiousness of bias
within health care contexts, a setting where
decision-making is often lauded for being objec-
tive, makes intervening in health care contexts
complicated (Burgess, Fu, and van Ryn 2004; Teal
et al. 2010). Providers, like the general population,
are now more likely to use subtle biases. Moreover,
stereotyping may be exacerbated by the fast-paced,
high-stress environment in which providers work
that all but necessitates clinicians to make general-
izations about various groups because of time con-
straints (Burgess et al. 2004). These generalizations
persist even as providers acknowledge that unmet
social needs result in worse health for all Americans
and are aware that structural factors, such as restric-
tive insurance policies, undermine health outcomes
(Metzl and Hansen 2014).

Manzer and Bell
131
Providers may be less likely to acknowledge
personal biases because they expect themselves to
be unaffected by patients’ sociodemographic char-
acteristics and often have a difficult time accepting
that they harbor prejudicial attitudes (van Ryn and
Fu 2003). Indeed, as the findings demonstrate, pro-
viders deny overtly coercing women to use contra-
ception. For example, most participants clearly
reject assertions of biased contraceptive counseling
because they believe in women’s right to choose
and feel they have found unbiased ways to facilitate
this choice within their appointments.
Medicine lends itself to denying individual pro-
vider bias as well as systemic discrimination. As a
profession and scientifically oriented discipline,
medicine garners much of its prestige from the
belief that it is both an objective science as well as
an altruistic profession (Freidson 1970). As our
findings demonstrate, health care providers can
deny prejudicial counseling by citing documented
trends in contraceptive use, pregnancy risk, and the
measurable consequences for unplanned pregnan-
cies among various sociodemographic groups that
justify targeted counseling behaviors. This target-
ing disproportionately affects black and Latinx
women as well as poor, young, and unmarried
women, who are most often cited as at risk for such
pregnancies. Indeed, in other areas of medicine,
providers similarly rely on objective measurements
and medical terminology such as obese quantified
by a patient’s body mass index rather than the more
subjective term fat to disguise what researchers
have established are often moral judgments about
patients’ individual behaviors (Murray 2008).
Doing so allows such judgments to operate without
question and often lends them medical legitimacy.
As Zestcott and colleagues (2016) find, addi-
tional work is needed to understand the conditions
in which provider bias is likely to affect communi-
cation with patients and subsequent prescribing
decisions. The participants elucidated several perti-
nent structural limitations within medicine, like
short appointments and increasingly large patient
rosters. These conditions require clinicians to con-
duct “social triaging,” assessing risk based on infor-
mation drawn from provider-initiated questions and
patient sociodemographic traits rather than from
quality dialogue. However, doing so produces and
reinforces stereotypes about patients that become
“commonsense” ways of prescribing contraception
to various demographic groups. For instance, as in
prior research, providers described a preference for
recommending LARCs to younger women, women
of color, and women of lower SES out of concern
for their pregnancy risk (Borrero et  al. 2009;
Dehlendorf et al. 2010). Rather than acknowledge
their individually held biases, providers evoked data
or used safe biases to disregard and/or justify their
own assumptions about pregnancy that influenced
many of their prescribing and counseling decisions.
This finding held across approaches—whether stan-
dardizing or implementing patient-centered care.
Importantly, by ascribing risk to behaviors, pro-
viders fail to position women’s behaviors within
the structural contexts in which they are situated.
However, making such connections is challenging.
Like Chapman and colleagues (2013) find, provid-
ers with insufficient time may resort to filling in
knowledge gaps about their patients with stereotype-
based information like race or relationship status
when evaluating pregnancy risk and contraceptive
appropriateness. Such findings have significant
consequences for women’s reproductive auton-
omy, especially that of women of color and lower
SES women.
Indeed, continued bias among providers who
prescribe contraception results in patient profiling
in the promotion of LARCs as a first-line or pre-
ferred method (Dehlendorf et al. 2018; Gomez et al.
2014). However, denying bias also neglects the
well-documented history of reproductive abuses
perpetrated against marginalized women in the
United States. We observed that well-intentioned
practitioners, some of whom even cited this history,
still held deep-seated judgments about whether
(more) children are acceptable for certain women.
Importantly, many of these stereotypes about who
should reproduce are artifacts of the United States’s
racialized and class-based policies and practices
that have stratified reproduction for decades (Colen
1986; Luna and Luker 2013; Roberts 1997).
Rather than assuming these biases do not exist,
clinicians would benefit from acknowledging biases
and identifying techniques to actively challenge and
undermine dominant stereotypes. Providers deserve
tools to help them respond when patients make dif-
ferent choices than they might prefer. One such
strategy is to do more within contraceptive initia-
tives to explicitly address individually held biases
around reproduction and connect them to the social
forces that engender them. Additionally, researchers
should reflect epistemologically on the impact of
their findings about pregnancy risk and contracep-
tive use on stoking and legitimizing provider biases
about who is fit to parent and which contraceptive is
preferred. Contextualizing their findings, for
instance, may mitigate such effects (Dehlendorf
et al. 2018).

132
Journal of Health and Social Behavior 62(2)
The persistence and prevalence of bias in health
care, and in contraceptive counseling more specifi-
cally, has significant implications for women’s
reproductive health and the provider–patient rela-
tionship. Like other research has found, we note that
provider biases affect some women’s health-seeking
behaviors over the life course. Indeed, many women
initiate contraception relatively young. Adverse
health care experiences during adolescence and
young adulthood can negatively impact future
health-seeking behavior, with important implications
for health later in life (Gomez and Wapman 2017).
Notably, the findings presented here are limited
to the sample from which they are drawn and can-
not be used to generalize about provider bias in
other settings or among other provider groups.
Additionally, it is beyond the scope of this article to
more systematically explore the role of specific
patient or provider sociodemographic characteris-
tics in determining provider counseling and contra-
ceptive prescribing. However, patient race-ethnicity,
SES, age, and relationship status frequently emerge
as key justifications for provider decision-making
that warrant ongoing attention.
Moreover, as evinced in the results, we found
biased care across all providers despite specializa-
tion, age, race, or gender. This may be due to the rel-
ative homogeneity among clinicians in terms of
upbringing and class status (see Alsan, Garrick, and
Graziani 2019; Carrese and Rhodes 1995). Providers
from higher SES backgrounds with similar medical
school education may hold shared beliefs about
parental readiness, parenting, and the appropriate-
ness of specific contraceptives for specific women
(Stevens 2015). Moreover, providers’ use of preg-
nancy risk data in their clinical decision-making may
further reinforce these personal beliefs. We also note
that white providers appeared to discuss their strate-
gies to avoid biased care more explicitly relative to
providers of color, although all providers did discuss
these strategies. The emphasis on race-based bias
trainings may hold some explanatory value. White
providers learning about discrimination against
racial minorities may be more inclined to reflect on
their own approaches and more consciously develop
coping strategies with which to navigate and mini-
mize bias. Both findings should be explored further
in future research to expand on work that currently
explores the relationship between race concordance
and patient care outcomes (see Alsan et  al. 2019;
Carrese and Rhodes 1995).
Provider bias remains a poignant issue in
American health care as well as within contracep-
tive counseling. As RJ activists and scholars
continue to underscore, bias and the social forces
that engender it persist and continue to dispropor-
tionately affect the reproductive autonomy of mar-
ginalized women as well as their health care
generally. Understanding how providers manage
this bias is key not only to minimizing differential
treatment within medicine but also to reducing
related health disparities and achieving reproduc-
tive justice for all women.
Acknowledgments
We extend our thanks to Brianna VanArsdale and Virginia
Berndt for their research assistance and discussion as we
conceptualized this article. We also thank the anonymous
reviewers and Miranda Waggoner for their thoughtful and
instructive comments on previous versions of the article.
Funding
The authors disclosed receipt of the following financial
support for the research, authorship, and/or publication of
this article: This work was supported by a research grant
from a private philanthropic foundation that had no
involvement in the analysis and interpretation of the data
or the decision to submit the article for publication.
ORCID iD
Jamie L. Manzer
 https://orcid.org/0000-0002-3111-
5578
Note
1.
We define implicit bias as a set of social stereotypes
about groups of people formed and held outside of
one’s awareness, whereas explicit biases are those
knowingly held (see Chapman et al. 2013).
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Author Biographies
Jamie L. Manzer is a PhD candidate in the Department of
Sociology & Criminal Justice at the University of
Delaware and holds a master’s of public policy degree.
Her research explores the intersections of reproduction,
health, inequality, and policy. She has published in

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journals such as Qualitative Health Research and Journal
of Interpersonal Violence.
Ann V. Bell is an associate professor of sociology at the
University of Delaware. Her research, centering on the
intersection of gender and health, specifically examines
processes, inequalities, and constructions of reproductive
health. Her work has examined the experiences of infertil-
ity, abortion, and contraception and how those experiences
are shaped by class- and race-based social understandings
of reproduction.

    Scientific Research Study Methods:

Answer: We recruited 51 contraceptive-counseling clinicians in one U.S. state—23 physicians, 19 nurse practitioners, eight nurse-midwives, and one nurse—from private and Title X clinics via phone, e-mail, and mailed invitations, then conducted 38- to 120-minute (mean ≈ 71 min) semi-structured interviews in 2018–2019 that probed prescribing patterns, patient interactions, and bias-mitigation tactics. We audio-recorded, verbatim-transcribed, de-identified, and coded all interviews in ATLAS.ti; a five-member research team used iterative, inductive grounded-theory procedures with paired transcript reviews to ensure intercoder reliability and thematic saturation. This approach allowed us to map four bias-management strategies—invoking scientific rationales, deploying “safe” social labels, standardizing counseling scripts, and invoking patient-centered choice—and to show how they can inadvertently reproduce inequities in contraceptive care.