Disorienting or Transforming? Using the Arts and Humanities to Foster Social Advocacy
Bibliographic record
Abstract
Introduction: The arts and humanities (AH) have transformative potential in medical education. Research suggests that AH-based pedagogies may facilitate both personal and professional transformation in medical learners, which may then further enhance the teaching and learning of social advocacy skills. However, the potential for such curricula to advance social advocacy training remains under-explored. Therefore, we sought to identify how AH may facilitate transformative learning of social advocacy in medical education. Methods: Building upon previous research, we conducted a critical narrative review seeking examples from the literature on how AH may promote transformative learning of social advocacy in North American medical education. Through a search of seven databases and MedEdPORTAL, we identified 11 articles and conducted both descriptive and interpretative analyses of their relation to key tenets of transformative learning, including: disorientation/dissonance, critical reflection, and discourse/dialogue. Results: We found that AH are used in varied ways to foster transformative learning in social advocacy. However, most approaches emphasize their use to elicit disorientation and dissonance; there is less evidence in the literature regarding how they may be of potential utility when applied to disorienting dilemma, critical reflection, and discourse/dialogue. Discussion: The tremendous potential of AH to foster transformative learning in social advocacy is constrained due to minimal attention to critical reflection and dialogue. Future research must consider how novel approaches that draw from AH may be used for more robust engagement with transformative learning tenets in medical education.
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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 | 0.027 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".