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Record W4391644090 · doi:10.7202/1108984ar

KNOWLEDGE AND ATTITUDES OF BACCALAUREATE SOCIAL WORK STUDENTS ABOUT INTERPROFESSIONAL COLLABORATION IN CANADA

2024· article· en· W4391644090 on OpenAlexvenueaboutno aff
Anna Azulai, Celina Vipond

Bibliographic record

VenueCanadian social work review · 2024
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsInterprofessional educationSocial workMedical educationHealth careWork (physics)Experiential learningPsychologyMedicinePedagogyPolitical science

Abstract

fetched live from OpenAlex

Although interprofessional collaboration is a common expectation in social work employment, interprofessional education has not been a robust feature of baccalaureate social work preparation in Canada. There is also a dearth of research on the topic. These gaps are problematic because social workers with baccalaureate degrees are often employed in interprofessional teams in various health care settings in Canada. To address this gap in knowledge, this mixed methods study explores attitudes toward interprofessional collaboration of social work students in a Canadian undergraduate university. Also, the study evaluates the students’ knowledge acquisition of interprofessional competencies after a single interprofessional education event. Findings indicate a positive change in students’ attitudes and enhanced knowledge of the interprofessional care competencies. The study contributes to the limited body of research on interprofessional education of baccalaureate-level social work students in Canada. It also shows the power of a single interprofessional experiential event in benefiting professional education of future social work professionals.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.454
Teacher spread0.423 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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