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Record W2284447677 · doi:10.3109/13561820.2015.1055717

Perceptions of interprofessionalism in health professional students participating in a novel community service initiative

2016· article· en· W2284447677 on OpenAlexafffundabout
Erica S. Tsang, Christopher C. Cheung, Todd Sakakibara

Bibliographic record

VenueJournal of Interprofessional Care · 2016
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of British Columbia
FundersUniversity of British ColumbiaDoctors of BC
KeywordsInterprofessional educationAutonomyDowntownPerceptionMedical educationScale (ratio)Health careNursingHealth professionsService (business)PsychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

Interprofessional collaboration is integral to effective patient care in today's healthcare system. Early exposure to other professions in a hands-on manner during education can be helpful for future practice. However, opportunities for interprofessional education are typically faculty driven and remain limited. Thirty-eight students from different health professions at the University of British Columbia worked collaboratively to promote cardiovascular risk reduction in Vancouver's Downtown Eastside. Student attitudes toward interprofessionalism were assessed using the Interdisciplinary Education Perception Scale (IEPS). While 38 participants (55%) completed the survey prior to participation in this initiative, only 21 individuals completed the follow-up survey After participation, there were significant improvements in the competency and autonomy (p = 0.02) and perception of actual cooperation (p = 0.04). Students did not report any difference in their perceived need for cooperation after participation in the initiative. These results suggest that student-led community service initiatives can be an effective method for interprofessional education amongst health professional students.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.081
GPT teacher head0.524
Teacher spread0.444 · 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 designObservational
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

Citations8
Published2016
Admission routes3
Has abstractyes

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