Pathways, journeys and experiences: Integrating curricular activities related to social accountability within an undergraduate medical curriculum
Bibliographic record
Abstract
BACKGROUND: Health professions education curricula are undergoing reform towards social accountability (SA), defined as an academic institution's obligation to orient its education, service and research to respond to societal needs. However, little is known about how or which educational experiences transform learners and the processes behind such action. For example, those responsible for the development and implementation of undergraduate medical education (UGME) programs can benefit from a deeper understanding of educational approaches that foster the development of competencies related to SA. The purpose of this paper was to learn from the perspectives of the various partners involved in a program's delivery about what curricular aspects related to SA are expressed in a UGME program. METHODS: We undertook a qualitative descriptive study at a francophone Canadian university. Through purposive convenience and snowball sampling, we conducted 16 focus groups (virtual) with the following partners: (a) third- and fourth-year medical students, (b) medical teachers, (c) program administrators (e.g., program leadership), (d) community members (e.g., community organisations) and (e) patient partners. We used inductive thematic analysis to interpret the data. RESULTS: The participants' perspectives organised around four key themes including (a) the definition of a future socially accountable physician, (b) socially accountable educational activities and experiences, (c) characteristics of a socially accountable MD program and (d) suggestions for curriculum improvement and implementation. CONCLUSIONS: We extend scholarship about curricular activities related to SA from the perspectives of those involved in teaching and learning. We highlight the relevance of experiential learning, engagement with community members and patient partners and collaborative approaches to curriculum development. Our study provides a snapshot of what are the sequential pathways in fostering SA among medical students and therefore addresses a gap between knowledge and practice regarding what contributes to the implementation of educational approaches related to SA. We emphasise the need for educational innovation and research to develop and align assessment methods with teaching and learning related to SA.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| 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".