204: Fostering Transformative Learning in a Social Pediatrics Research Summer Studentship Through Empowerment and Assessment
Bibliographic record
Abstract
Medical educators are challenged to integrate new strategies to prepare future pediatricians with the necessary skills to address health disparities (Ford-Jones et al, 2008). In response, the Social Pediatrics Research Summer Studentship (SPReSS) program was developed and implemented for medical students at the University of Toronto. Transformative learning principles were applied to the program and curriculum design to facilitate critical reflection and learning, and as an innovative approach to program development and evaluation. The curriculum consisted of research and clinical placements, as well as a formal seminar series. Students were asked to write a reflection describing a situation that challenged their thinking or caused them to re-evaluate previously held attitudes or perceptions. The reflections were assessed with a rubric, and the program was evaluated through thematic analysis of the reflections and an exit survey of faculty and students. The analysis revealed that students were preoccupied by their empathic responses to marginalized patients. They described feeling empowered to act as advocates and that these feelings were reinforced through assigned readings or role modeling of faculty. Students found the program both challenging and rewarding, particularly the integration of a clinical and research experience. Faculty found students to be engaged and reflective, making connections between assigned readings and practical experiences. The theory of transformative learning can be applied to medical education programs and curricula to help students identify with advocacy and develop the necessary critical reflective and advocacy skills to address health disparities. A key strategy to fostering transformative learning is the incorporation of an authentic assessment tool such as the rubric to encourage and evaluate student reflections.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".