The Goldilocks of Social Justice Education: Balint Groups as a Curricular Intervention to Support Equitable Health Care
Bibliographic record
Abstract
While there is consensus within the medical profession on the importance of ensuring future physicians are well versed in issues of social justice, there is little consensus on how to best achieve this. Traditional methods of didactic lectures or case-based learning, with an emphasis on the transmission of knowledge, run the risk of reinforcing the very inequities they are aiming to disrupt. The classroom experiences do not call on trainees to act on issues of social justice beyond discussing imagined actions in a carefully constructed case. Balint Groups offer an alternate pedagogy that align with a more interpretative style of teaching and offer an opportunity for meaningful engagement with issues of social justice. In Balint Groups, students are engaged in cases where the presenter has participated directly in the clinical encounter. While these cases tend to focus on relational dilemmas between the doctor and patient, the dilemma can also highlight an internal dilemma between competing professional identities - such as the biomedical expert and the socially conscious professional. Imagined agency is removed and the group is tasked with reflecting on the dissonance created by these two competing identities. While the use of Balint Groups as a curricular intervention offers exciting opportunities to promote social justice, there are cautions. First, Balint Groups operate within the dominant discourse of medical education and facilitators must be sensitive to how this may position the presenter; second, it cannot be forced - it must arise from the case presented.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".