Social Justice and Health Equity in the Teaching and Learning Environment: Perspectives of Academic Leaders in Health Profession Education Programmes
Bibliographic record
Abstract
It is the responsibility of all health profession education programmes to prepare their graduates to champion social justice and health equity (SJ/HE) within and beyond the healthcare system. However, little is known about the perspectives of educational leaders within health professional programmes regarding the teaching and learning environment (TLE) with respect to SJ/HE. The objective of this study was to explore the perspectives of health profession education leaders about their individual and collective vision for a TLE that promotes SJ/HE and its actualization. A qualitative descriptive approach was utilized to gather, synthesize and make meaning of the perspectives of academic leaders in one Canadian health professional faculty. Using semi-structured interviews, participants (n=14) representing five different colleges including medicine, nursing, oral health, pharmacy, and rehabilitation sciences, were interviewed in-person in the academic setting. Following inductive thematic analysis, one overarching theme resulted, “We Need to Walk the Talk.” Five sub-themes also emerged, including understanding of SJ/HE; the current TLE; facilitators and barriers to a TLE promoting SJ/HE; and actions required to further develop a TLE promoting SJ/HE. Academic leaders expressed hope and willingness to create a TLE that promotes SJ/HE, acknowledging that there was a lot to be done and a unified vision for the faculty is important. The results of this study underscore the need for academic leaders to have a clear and unified articulation of a TLE that embodies SJ/HE for all.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".