Unpacking educational approaches for social accountability in health professions education: a scoping review
Bibliographic record
Abstract
Health systems worldwide continue to face health inequities rooted in social, political, and economic structures. In response, health professions education (HPE) programs are increasingly aiming to prepare learners for socially accountable practice. Despite this growing attention, social accountability remains ambiguously defined and inconsistently taught, assessed, and enacted across curricula. The aim of this scoping review was to map the breadth and depth of educational approaches within HPE that promote social accountability. A scoping review was conducted following Arksey and O'Malley's six-stage framework and the Joanna Briggs Institute methodology. MEDLINE, EMBASE, CINAHL, ERIC, APA PsycINFO, and Education Source were searched for articles published from January 2000 to May 2020, with an updated search in 2024. Screening involved a calibration exercise at both title/abstract and full-text stages. Descriptive numerical analysis and reflexive thematic analysis were employed, supported by knowledge user consultation. Most articles originated in North America (n = 179; 82%) and focused on undergraduate medical education (n = 157; 71.7%), especially among future physicians (n = 122; 55.7%). Common strategies included reflection-based learning (n = 90; 41.1%), small group learning (n = 82; 37.4%), and didactic lectures (n = 80; 36.5%). Many studies described using multimodal approaches (n = 85; 38.8%). Educational approaches emphasized experiential, reflective, and community-engaged learning and were grouped into ten overarching categories. These were organized using the Guideline for Reporting Evidence-based practice Educational interventions and Teaching (GREET). By synthesizing how educational approaches to social accountability are reported, this review offers a basis for reflection, dialogue, and further inquiry into their development, delivery, and integration in HPE curricula.
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 | 0.051 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.041 | 0.034 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".