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Record W3167513314 · doi:10.36834/cmej.71420

Community organization feedback about an undergraduate medical education service learning program

2021· article· en· W3167513314 on OpenAlexaffvenueabout
Roger Berrington, Nina Condo, Felicien Rubayita, Karen Severud Cook, Chelsea Jalloh

Bibliographic record

VenueCanadian Medical Education Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsResearch ManitobaUniversity of ManitobaGovernment of Manitoba
Fundersnot available
KeywordsService-learningPrivilege (computing)Medical educationCurriculumService (business)Library sciencePsychologyMedicinePedagogyPolitical scienceComputer scienceBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: In 2016, Service Learning (SL) became a curricular requirement for undergraduate medical education (UGME) students at the University of Manitoba. Students partner with a community-based organization for two years to engage in non-clinical activities in community settings. Significant feedback has been collected from students re: their SL experiences. This project specifically collected feedback from community organizations involved with SL. METHODS: In June 2019, an electronic survey was distributed to the 36 community organizations involved with SL. RESULTS: Twenty-seven organizations completed the survey. Feedback was grouped into two main themes: 1) Logistics and 2) The SL Experience. About half (52%) of respondents indicated it was "easy" to schedule students for SL; however, students' busy schedules and differences between hours of organization programming and students' availability were highlighted. Most respondents described students as "engaged" (70%); respondents indicated SL raised students' understanding of power and privilege (56%) and systemic oppression (63%). CONCLUSIONS: Community organizations shared valuable insights to inform the SL program. Results identified specific aspects of the SL program to address moving forward, such as sharing learning objectives with community partners. Ensuring processes are in place to obtain feedback from community partners is an essential step to improve SL programs, and to strengthen reciprocal community-university partnerships.

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)0.004
metaresearch head score (Gemma)0.031
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0400.000

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 head0.022
GPT teacher head0.343
Teacher spread0.321 · 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 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

Citations6
Published2021
Admission routes3
Has abstractyes

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