Lessons learned in global family medicine education from a Besrour Centre capacity-building workshop
Bibliographic record
Abstract
At a global level, institutions and governments with remarkably different cultures and contexts are rapidly developing family medicine centred health and training programmes. Institutions with established family medicine programmes are willing to lend expertise to these global partners but run the risk of imposing a postcolonial, directive approach when providing consultancy and educational assistance. Reflecting upon a series of capacity building workshops in family medicine developed by the Besrour Centre Faculty Development Working Group, this paper outlines approaches to the inevitable challenges that arise between healthcare professionals and educators of differing contexts when attempting to share experience and expertise. Lessons learned from the developers of these workshops are presented in the desire to help others offer truly collaborative, context-centred faculty development activities that help emerging programmes develop their own clinical and educational family medicine frameworks. Established partner relationships, adequate preparation and consultation, and adaptability and sensitivity to partner context appear to be particularly significant determinants for success.
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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 | 0.038 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".