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Record W2037584934 · doi:10.3109/13561820.2014.917403

Interprofessional socialization as a way to introduce collaborative competencies to first-year health science students

2014· article· en· W2037584934 on OpenAlexaff
Margarita V. DiVall, Leslie Kolbig, Mary Carney, Jennifer L. Kirwin, Christine Letzeiser, Shan Mohammed

Bibliographic record

VenueJournal of Interprofessional Care · 2014
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsHealth Sciences North
Fundersnot available
KeywordsInterprofessional educationCornerstoneMedical educationSocializationCore competencyInclusion (mineral)CurriculumPharmacyHealth scienceHealth careRelevance (law)PsychologyNursingMedicinePedagogyPolitical science

Abstract

fetched live from OpenAlex

Interprofessional education (IPE) is the cornerstone of preparing future health care providers but remains to be a challenge for many health science programs. We aimed to develop and evaluate an interprofessional conference for first-year health science students with goals to provide students with interprofessional socialization opportunity and introduce IPE principles. A half-day conference was based upon core competencies for health professionals and involved 277 first-year health sciences, nursing, pharmacy, physical therapy, and speech language pathology and audiology students. Alcohol and substance misuse was chosen as a topic for its relevance to college students and health professionals. Results from program evaluation revealed that the conference was successful in exposing students to core interprofessional competencies and provided useful information about alcohol and substance misuse. This study advocates for early inclusion of IPE in the health professions curricula in the form of interprofessional socialization.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0020.009
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.462
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations29
Published2014
Admission routes1
Has abstractyes

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