Responsiveness to Societal Needs in Medical Education: Examining Context for Institutional Actions
Bibliographic record
Abstract
Responsiveness to societal needs is an expectation for academic institutions (medical schools and teaching hospitals) that encompasses their three missions – education, research and service to patients and populations. This paper presents a scholarly perspective that proposes practical courses of action for academic institutions to operationalise calls by the World Health Organization and others for medical education institutions to demonstrate societal responsiveness. We offer a pragmatic framework for institutional action to guide societal responsiveness initiatives in all domains of an institution’s academic mission. We point to the history of social accountability as a core role of academic institutions and how these early approaches provide a model for present-day actions and activities. We discuss the importance of engaging individuals and groups who benefit from institutional actions in the service of social accountability in co-determining optimal courses of action. We offer concrete recommendations in each domain of the academic mission to create a practical, institution-specific approach for societal responsiveness, shaped by the given organization’s mission and its role in addressing education, health care and research needs at the level(s) (local, regional or national) at which it operates. We discuss the local, national and global contexts in which individual institutions operate and how they create facilitators and barriers for institutions seeking to meet social responsiveness mandates. We close with discussing how focusing on institution-level priorities for societal responsiveness allows for meaningful actions in a range of settings within an increasingly complex and challenging environment in many regions around the globe.
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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 | 0.045 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.047 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.003 | 0.037 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".