Fostering Transformative Learning in a Social Pediatrics Research Summer Studentship
Bibliographic record
Abstract
PROBLEM: Teaching future doctors the skills necessary to address health disparities is a challenge for medical educators. In response, the authors developed and implemented the Social Pediatrics Research Summer Studentship (SPReSS) program for medical students at the University of Toronto. APPROACH: The curriculum incorporated research and clinical placements into a formal seminar series. Participating students were required to complete a research project and to write a reflection describing a situation that challenged their thinking. The authors and curriculum developers applied transformative learning principles not only to facilitate critical reflection and learning in the students but also as an innovative approach to program development and evaluation. The authors conducted a thematic analysis of the reflections of 23 students participating in the program in June and July 2013, 2014, and 2015 to evaluate the SPReSS program. OUTCOMES: The analysis revealed students' empathic responses to marginalized patients, and these responses acted as triggers for critical reflection. Students described feeling empowered to act as advocates and wrote that these feelings were reinforced through faculty members' role modeling. According to their reflections, students found the program both challenging and rewarding, particularly the integration of the clinical and research experiences which made broader sociopolitical phenomena introduced through assigned readings and seminar discussions concrete. NEXT STEPS: The authors are exploring models, including a fourth-year selective or multiyear longitudinal experience, to support more students. They also hope to involve more community partners and to evaluate long-term outcomes of participants.
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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 | 0.032 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.003 | 0.006 |
| 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".