Strengthening Faculty Development through Regional and Global Collaboration: An Innovative Virtual Program in Cambodia
Bibliographic record
Abstract
Background: Cambodia faces significant shortages of qualified physicians and skilled faculty, posing challenges for both medical training and educational reform. Innovative faculty development initiatives are essential in resource‑limited settings to address these gaps and support broader reforms. Objectives: We describe a novel virtual faculty development program, created through regional and global collaboration, to enhance medical education in Cambodia. Methods: We conducted a faculty development program consisting of six virtual workshops between August and September 2023. The program targeted basic science faculty in the pre‑clinical curriculum, introducing integrated teaching, case‑based learning methods, and related assessment strategies. Regional and international collaboration formed the backbone of the initiative, involving expertise from neighboring Vietnam and the United States. Findings: Although participants were voluntary, and we offered no financial incentives, over 80% of the invited participants attended the sessions. Participants reported high satisfaction with the workshop content and format. Additionally, participants applied the concepts learned, as demonstrated by their creation and use of integrated clinical cases in their teaching. Conclusions: Medical education is increasingly becoming a focus of global health collaboration. Findings highlight the feasibility and benefits of a partnership‑driven virtual faculty development model. By leveraging regional and international expertise to address local challenges, this initiative contributed to strengthening faculty capacity in resource‑constrained settings.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".