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Record W4213047612 · doi:10.1080/0142159x.2022.2035339

Community engagement by faculties of medicine: A scoping review of current practices and practical recommendations

2022· article· en· W4213047612 on OpenAlexaff

Bibliographic record

VenueMedical Teacher · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCommunity engagementMEDLINECurrent (fluid)Public engagementCommunity of practice

Abstract

fetched live from OpenAlex

PURPOSE: Social accountability (SA) is the responsibility of faculties of medicine (FoMs) to address the health priorities of the communities they serve. Community engagement (CE) is a vital, but often ambiguous, component of SA. Practical guidance on how to engage community partners (CPs) is key for meaningful CE. We conducted a systematic scoping review of CE involving FoMs to map out how FoMs engage their communities, to provide practical recommendations for FoMs to take part in CE, and to highlight gaps in the literature. MATERIALS AND METHODS: We searched electronic databases for articles describing projects or programs involving FoMs and CPs. Descriptive information was analyzed thematically. RESULTS: Thirty-eight of 1406 articles were included, revealing three themes: (1) Partners (Who to Engage)-deciding who to engage establishes the basis for responsibility and creates space for communities to engage FoMs; (2) Partnerships (How to Engage)-fostering creative and authentic collaboration, enabling meaningful community contributions; and (3) Projects and Programs (With What to Engage)-identifying opportunities for communities to have a voice in many spaces within FoMs. Under these themes emerged 32 practical recommendations. CONCLUSION: Practical guidance facilitates meaningful commitments to communities. The literature is rich with examples of community-FoM partnerships. We provide recommendations for CE that are clear, evidence-based, and responsive.

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)0.019
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.679
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0120.000

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 head0.345
GPT teacher head0.523
Teacher spread0.177 · 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.

Study designNot applicable
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

Citations5
Published2022
Admission routes1
Has abstractyes

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