A mapping of health education institutions and programs in the WHO African Region
Bibliographic record
Abstract
<ns3:p> <ns3:bold>Background:</ns3:bold> Information on health education institutions is required for planning, implementing and monitoring human resources for health strategies. Details on the number, type and distribution of medical and health science programs offered by African higher education institutions remains scattered. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> We merged and updated datasets of health professional and post-graduate programs to develop a mapping of health education institutions covering the World Health Organization African Region as of 2021. </ns3:p> <ns3:p> <ns3:bold>Results:</ns3:bold> Nine hundred and nine (909) institutions were identified in the 47 countries. Together they offered 1,157 health professional programs (235 medicine, 718 nursing, 77 public health and 146 pharmacy) and 1,674 post-graduate programs (42 certificates, 1,152 Master’s and 480 PhDs). Regionally, East Africa had the most countries with multiple academic health science centres - institutions offering medical degrees and at least one other health professional program. Among countries, South Africa had the most institutions and post-graduate programs with 182 and 596, respectfully. A further five countries had between 53-105 institutions, 12 countries had between 10 and 37 institutions, and 28 countries had between one and eight institutions. One country had no institution. Countries with the largest populations and gross domestic products had significantly more health education institutions and produced more scientific research (ANOVA testing). </ns3:p> <ns3:p> <ns3:bold>Discussion:</ns3:bold> We envision an online database being made available in a visually attractive, user-friendly, open access format that nationally, registered institutions can add to and update. This would serve the needs of trainees, administrators, planners and researchers alike and support the World Health Organization’s <ns3:italic>Global strategy on human resources for health: workforce 2030</ns3:italic> . </ns3:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".