Development and Launch of the First Obstetrics and Gynaecology Master of Medicine Residency Training Programme in Botswana
Bibliographic record
Abstract
Abstract Background: Sub-Saharan Africa (SSA) faces a severe shortage of Obstetrician Gynaecologists (OBGYNs). While the Lancet Commission for Global Surgery recommends 20 OBGYNs per 100,000 population, Botswana has only 40 OBGYNs for a population of 2.3 million. We describe the development of the first OBGYN Master of Medicine (MMed) training programme in Botswana to address this human resource shortage. Methods: We developed a curriculum for a 4-year OBGYN MMed at the University of Botswana (UB). We benchmarked curriculum content, learning outcomes, competencies, assessment strategies and research requirements with regional and international programmes. We engaged relevant local stakeholders and developed international collaborations to support in-country subspecialty training. Results: The OBGYN MMed curriculum was completed and approved by all relevant UB bodies within ten months during which time additional staff were recruited and programme financing was assured. The programme was advertised immediately; 26 candidates applied for four positions, and all selected candidates accepted. The programme was launched in January 2020 with government salary support of all residents. The national accreditation process was initiated. Conclusion: Training OBGYNs in-country has many benefits to health systems in SSA. Curricula can be adjusted to local resource context yet achieve international standards through thoughtful design and purposeful collaborations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".