Medical mentorship in Afghanistan: How are military mentors perceived by Afghan health care providers?
Bibliographic record
Abstract
BACKGROUND: Previous work has been published on the experiences of high-resource setting physicians mentoring in low-resource environments. However, not much is known about what mentees think about their First World mentors. We had the opportunity to explore this question in an Afghan Army Hospital, and we believe this is the first time this has been studied. METHODS: We conducted a pilot cross-sectional survey of Afghan health care providers evaluating their Canadian mentors. We created a culturally appropriate 19- question survey with 5-point Likert scores that was then translated into the local Afghan language. The survey questions were based on domains of Royal College of Physicians and Surgeons of Canada's CanMEDS criteria. RESULTS: The survey response rate was 90% (36 of 40). The respondents included 13 physicians, 21 nurses and 2 other health care professionals. Overall, most of the Afghan health care workers felt that working with mentors from high-resource settings was a positive experience (median 4.0, interquartile range [IQR] 4-4), according to CanMEDS domains. However, respondents indicated that the mentors were reliant on medical technology for diagnosis (median 5.0, IQR 4-5) and failed to consider the limited resources available in Afghanistan. CONCLUSION: The overall impression of Afghan health care providers was that mentors are appropriate and helpful. CanMEDS can be used as a framework to evaluate mentors in low-resource conflict environments.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".