Extending social accountability mandates to biomedical research in Canadian faculties of medicine
Bibliographic record
Abstract
Background: Social accountability (SA), as defined by Boelen and Heck, is the obligation of medical schools to address the needs of communities through education, research and service activities. While SA is embedded within health profession education frameworks in medicine, they are rarely taught within graduate-level (MSc/PhD) education. Methods: As these programs train future medical researchers, we invited first-year graduate students enrolled in a mandatory professionalism class at our institution (n = 111) to complete a survey on their perceptions of the importance of SA in their research, training, and future careers. Results: Over 80% (n = 87) of respondents agreed that SA is relevant and felt committed to integrating it into their future research activities, only a limited number of students felt confident and/or supported in their abilities to integrate SA into their research. Conclusions: Specific SA training in graduate education is necessary for students to effectively incorporate elements of SA into their research, and as such support the SA mandates of their training institutions. We posit that awareness of SA principles formalizes the professional standards for biomedical researchers and is thus foundational for developing a professionalism curriculum in graduate education programs in medicine. We propose an expansion of the World Health Organization (WHO) partnership pentagon to include partners within the research ecosystem (funding partners, certification bodies) that collaborate with biomedical researchers to make research socially accountable.
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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 | 0.170 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.036 | 0.020 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.006 | 0.008 |
| 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".