Attitudes towards sub-domains of professionalism in medical education: defining social accountability in the globalizing world
Bibliographic record
Abstract
BACKGROUND: Unmet health needs of populations around the world are a major contributor to lagging health outcomes globally. Medical professionals have a duty to address the health needs of their communities. In a globalizing world, the needs may seem limitless. Yet, most training involves immersion in one health system and its resources. How do practitioners reconcile this potentially limitless demand with their focused training and in understanding their duty to the populations they serve? METHODS: A mixed-method design was used. We distributing a pre-validated survey examining all facets of professionalism to the Department of Family Medicine at the University of Ottawa. This was followed by interviewing a purposive sample of residents and faculty with different levels of interest in working with underserved populations, to examine attitudes towards social accountability. RESULTS: Quantitative results did not replicate the factor structure of the pre-validated survey in our cohort. This and other gaps in individual responses were used to construct an interview guide. Interviews revealed differences between residents and faculty. Residents were likely to see social accountability as flowing from personal interest as opposed to a professional duty; and residents' sense of duty can be met through good care of individual patients under their sphere of care. Faculty were more likely to discuss facets of care that they could influence at the health system level nationally and beyond. CONCLUSION: More usable and succinct instruments are needed to capture individual attitudes on social accountability. Our results identify how new physicians in family medicine in Ottawa, Canada wish to apply learning in global health to local needs, responding to the call to "think global, act local."
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".