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Record W1789966254 · doi:10.22230/jripe.2012v2n3a80

Student-Run Clinics: Opportunities for Interprofessional Education and Increasing Social Accountability

2012· article· en· W1789966254 on OpenAlexaffvenueabout
Maxine Holmqvist, Carole Courtney, Ryan Meili

Bibliographic record

VenueJournal of Research in Interprofessional Practice and Education · 2012
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of SaskatchewanUniversity of Manitoba
Fundersnot available
KeywordsAccountabilityEquity (law)TeamworkMedicineMedical educationService-learningHealth careSocial workNursingInterprofessional educationPublic relationsPsychologyPedagogyPolitical science

Abstract

fetched live from OpenAlex

Background: Collaborative practice is a necessary component of providing effective, socially responsive, patient-centred care; however, effective teamwork requires training. Canadian student-run clinics are interprofessional community service-learning initiatives where students plan and deliver clinical and health promotion services, with the assistance of licensed healthcare professionals.Methods and Findings: In this article, we use a reflective approach to examine the phenomenon of student-run clinics in Canada. First, we briefly review the history of student-run clinics and then describe one particular clinic in detail. Then, drawing on the experiences of student-run clinics across the country, we identify common themes and challenges that we believe characterize these programs.Conclusion: Student-run clinics in Canada emphasize health equity, interprofessionalism, and student leadership. As more student-run clinics are developed, both nationally and internationally, co-ordinated research efforts are needed to determine their effects on students, institutions, communities, and healthcare systems. If educators can learn to collaborate effectively with student leaders, student-run clinics may be ideal sites for advancing learning around interprofessionalism and social accountability.

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)0.018
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.002
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 head0.253
GPT teacher head0.640
Teacher spread0.387 · 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

Citations46
Published2012
Admission routes3
Has abstractyes

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