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Record W4411631641 · doi:10.62694/efh.2025.294

Applying quality and equity lenses to advance social accountability in medical education

2025· article· en· W4411631641 on OpenAlexaff
Sophia Myles, Chandelle Mensour, Kerri Z. Delaney, Erin Cameron

Bibliographic record

VenueEducation for Health · 2025
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsThunder Bay Regional Health Sciences CentreNOSM UniversityLaurentian University
Fundersnot available
KeywordsAccountabilityEquity (law)Quality (philosophy)Social equalityOptometryBusinessPsychologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Social accountability has become a key driver of change within health professional education programs worldwide. Over the last two decades, there has been a growing number of frameworks, tools, and standards to help measure social accountability. This paper shares the experiencesand lessons learned from one of the institutions that participated in piloting the Institutional Self-Assessment Social Accountability Tool (ISAT). We argue that tools for measuring social accountability are valuable not only because they provide data but, more importantly, because they can embed a dialogic and critical reflective culture within institutions. We describe how the tool expanded our thinking about social accountability to advance it, including what constitutes socially accountable research and who and what fields contribute to this work. We argue that the tool encouraged collaborative critical reflexive practice and offered a process that was incorporated into institutional processes and activities, and simultaneously fostered intra-institutional cooperation. Finally, we contend that the application of quality and equity lenses offers sources of sustainability to advance social accountability in medical education as they encourage the prioritization of social accountability processes to achieve desired outcomes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.890
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.070
GPT teacher head0.567
Teacher spread0.498 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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