What are the family medicine faculty development needs of partners in low- and middle-income countries?
Bibliographic record
Abstract
The WHO endorses family medicine (FM) globally to improve health outcomes. The Besrour Centre (BC) brings together partners from low- and middle-income countries (LMICs) to collaborate on FM development in different contexts. Faculty development is an identified area of need, but specific needs were unknown. A qualitative study was conducted using two 1-1.5-hour focus groups at the 2015 BC conference. Ten countries and 12 universities were represented. Transcripts from semi-structured interviews were analysed for themes using a descriptive approach. There was unanimous support for the need for faculty development tools and resources, particularly in teaching skills. Most programmes lacked formal structure or funding. A consistently identified concept was how to teach specialist faculty the FM context, as was the importance of FM perspective to inform government policies. The need for faculty development of FM in LMICs is strong. FM faculty development resources can be expanded and shared through global health networks. Further expansion of faculty development workshops and toolkits is recommended. This study adds to the current knowledge because it helps to identify the gaps and priorities, specifically focused on LMICs, when developing faculty development FM programmes.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".