Problems of problem-based learning: Towards transformative critical pedagogy in medical education
Bibliographic record
Abstract
Problem-based medical education is based in a biomedical worldview that works to entrench deterministic ways of thinking about socioculturally-influenced health disparities in the minds of medical trainees. This perspective paper considers the utility of Paolo Freire's critical pedagogy as a means of redressing this issue, as it may enable medical learners to perceive and address the social sources of illness that shape their patients' lives. With an eye to advancing health equity, and educating health professionals who are responsive to marginalized and vulnerable communities, this paper considers how a problem-posing medical education could redefine physicians' relationships to knowledge, identity, and to their patients.
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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 | 0.042 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.084 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".