Datasets:

electricsheepafrica
/

africa-synth-disability-rehabilitation-workforce-training-all

license: cc-by-4.0
task_categories:
  - tabular-classification
language:
  - en
tags:
  - disability
  - rehabilitation
  - workforce
  - physiotherapy
  - task-shifting
  - synthetic
  - sub-saharan-africa
pretty_name: Rehabilitation Workforce & Training (SSA)
size_categories:
  - 10K<n<100K
configs:
  - config_name: urban_training_institution
    data_files: data/rehab_wf_urban.csv
    default: true
  - config_name: district_service_delivery
    data_files: data/rehab_wf_district.csv
  - config_name: rural_task_shifted
    data_files: data/rehab_wf_rural.csv
data_type: synthetic

⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

Rehabilitation Workforce & Training in Sub-Saharan Africa

Abstract

Synthetic dataset modelling rehabilitation workforce cadres, training, competencies, retention, and service delivery across three tiers in SSA. 0.5-2 rehab professionals per 100K vs 60+ in HIC; <5000 physiotherapists for all SSA.

Parameterization Evidence

Parameter	Value	Source	Year
0.5-2 rehab professionals per 100K in SSA	Workforce	WHO Rehab 2030	2023
<5000 physiotherapists for all SSA	Density	WCPT	2022
~0.5 P&O per million in SSA	Specialist	ISPO	2022

Validation

Usage

from datasets import load_dataset
ds = load_dataset("electricsheepafrica/rehabilitation-workforce-training", "urban_training_institution")

References

WHO. Rehabilitation 2030 initiative. 2023.
WCPT. Physiotherapy workforce data. 2022.
ISPO. Prosthetics and orthotics workforce. 2022.

Citation

@dataset{electricsheepafrica_rehabilitation_workforce_training_2025,
  title={Rehabilitation Workforce and Training in Sub-Saharan Africa},
  author={Electric Sheep Africa},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/electricsheepafrica/rehabilitation-workforce-training}
}

License

CC-BY-4.0