Teaching poverty and health: importing transformative learning into the structures and paradigms of medical education
Bibliographic record
Abstract
Background: As a paradigm of education that emphasizes equity and social justice, transformative education aims to improve societal structures by inspiring learners to become agents of social change. In an attempt to contribute to transformative education, the University of Toronto MD program implemented a workshop on poverty and health that included tutors with lived experience of poverty. This research aimed to examine how tutors, as members of a group that faces structural oppression, understood their participation in the workshop. Methods: This research drew on qualitative case study methodology and interview data, using the concept of transformative education to direct data analysis and interpretation. Results: Our findings centred around two broad themes: misalignments between transformative learning and the structures of medical education; and unintended consequences of transformative education within the dominant paradigms of medical education. These misalignments and unintended consequences provided insight into how courses operating within the structures, hierarchies and paradigms of medical education may be limited in their potential to contribute to transformative education. Conclusions: To be truly transformative, medical education must be willing to try to modify structures that reinforce oppression rather than integrating marginalized persons into educational processes that maintain social inequity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.050 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".