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Record W2953249340 · doi:10.15694/mep.2019.000141.1

Service-learning Curriculum Design and Implementation at the University of Toronto Faculty of Medicine

2019· article· en· W2953249340 on OpenAlexaffabout
Leedan Cohen, Fok‐Han Leung, Chika Oriuwa, Roxanne Wright

Bibliographic record

VenueMedEdPublish · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCurriculumMedical educationService-learningService (business)Community serviceInstitutionMedicineSociologyPedagogyPolitical sciencePublic relationsBusinessSocial science

Abstract

fetched live from OpenAlex

This article was migrated. The article was marked as recommended. Community service-learning is an integral component of the undergraduate medical experience, as it provides students with the opportunity to respond to and address societal issues. Students at the University of Toronto, Faculty of Medicine have traditionally participated in a service-learning curriculum that required them to choose placement opportunities from a centrally- developed catalogue of options, with no continuity between the university and the community from year to year. The mandatory service-learning placement was re-designed under the advisement of long-standing community partners, community-engaged physicians, and academics. The new model centralizes the relationship between faculty tutors and community partners, who act as co-educators for the medical students, with tutors serving as the primary link to community organizations. The University of Toronto's Faculty of Medicine is the first Canadian medical institution to implement this innovative curricular model.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.309
Teacher spread0.278 · 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 designQualitative
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

Citations4
Published2019
Admission routes2
Has abstractyes

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