Training for health services and systems research in Sub-Saharan Africa - a case study at four East and Southern African Universities
Bibliographic record
Abstract
BACKGROUND: The need to develop capacity for health services and systems research (HSSR) in low and middle income countries has been highlighted in a number of international forums. However, little is known about the level of HSSR training in Sub-Saharan Africa (SSA). We conducted an assessment at four major East and Southern African universities to describe: a) the numbers of HSSR PhD trainees at these institutions, b) existing HSSR curricula and mode of delivery, and c) motivating and challenging factors for PhD training, from the trainees' experience. METHODS: PhD training program managers completed a pre-designed form about trainees enrolled since 2006. A desk review of existing health curricula was also conducted to identify HSSR modules being offered; and PhD trainees completed a self-administered questionnaire on motivating and challenging factors they may have experienced during their PhD training. RESULTS: Of the 640 PhD trainees enrolled in the health sciences since 2006, only 24 (3.8%) were in an HSSR field. None of the universities had a PhD training program focusing on HSSR. The 24 HSSR PhD trainees had trained in partnership with a university outside Africa. Top motivating factors for PhD training were: commitment of supervisors (67%), availability of scholarships (63%), and training attached to a research grant (25%). Top challenging factors were: procurement delays (44%), family commitments (38%), and poor Internet connection (35%). CONCLUSION: The number of HSSR PhD trainees is at the moment too small to enable a rapid accumulation of the required critical mass of locally trained HSSR professionals to drive the much needed health systems strengthening and innovations in this region. Curricula for advanced HSSR training are absent, exposing a serious training gap for HSSR in this region.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".