Family Medicine needs assessment: Studying the clinical work of general practitioners in Ethiopia
Bibliographic record
Abstract
Background and Objective: Some universities in sub-Saharan Africa have initiated Family Medicine (FM) residency programs. This study was conducted by FM colleagues at Addis Ababa University (AAU) in Ethiopia and the University of Toronto, Canada to inform the FM residency curriculum at AAU. It was designed to determine the clinical problems that family physicians in Ethiopia may encounter. Methods : We used a mixed methods approach: Modified time-motion study and brief interviews. We observed 46 general practitioners (GPs) across ten sites in Ethiopia. Trained observers recorded time-motion data while GPs conducted their daily work. This data was supplemented by brief interviews with the GPs. Findings : Clinical encounters occupied 82% of GP work. The common symptoms were digestive-abdominal pain (21% visits), respiratory-cough (16%), and general-fever and chills (16%). The common diagnoses were infectious (22% visits), genitourinary (12%), circulatory (10%), and endocrine (10%). Challenges identified were lack of clinical resources (57% of GPs), difficulties in communication (48%) and excessive workload (33%). Most common requests were for information technology (78%) and HIV (46%) training. Conclusion : The profile of common symptoms and diagnoses indicated the competencies family physicians in the regions should have. This information will be used to develop an appropriate FM curriculum at AAU.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".