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Record W4386726781 · doi:10.1080/13561820.2023.2253845

Interprofessional socialization: a concept analysis

2023· review· en· W4386726781 on OpenAlexaff
Sara Dolan, Lorelli Nowell

Bibliographic record

VenueJournal of Interprofessional Care · 2023
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSocializationIdentity (music)Interprofessional educationValue (mathematics)Health carePsychologyMedical educationMedicineSocial psychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

, conducted using the Walker and Avant approach. Our literature search resulted in 27 publications with meaningful insights regarding interprofessional socialization, published between 1994-2022. We identified five defining attributes of interprofessional socialization: (a) learning about other professionals and the roles they play on interprofessional teams, (b) recognizing the value of collaborating with other professionals, (c) identifying a common goal shared across professions, (d) breaking down barriers between professional silos, and (e) developing dual identity: a professional identity and an interprofessional identity. We identified antecedents, consequences, empirical referents, and cases to better illustrate the concept. Insights from this concept analysis provided the foundation for a conceptual definition. Interprofessional socialization is an iterative process in which members from different professions come together to learn about and value each other's perspectives and contributions, while dispelling misconceptions and prejudices, continuously working toward formation of a dual identity: one for professional identity and one for interprofessional identity. Future research is needed to explore how interprofessional socialization changes over the course of a career and how efforts to increase interprofessional socialization across healthcare settings might impact interprofessional initiatives throughout healthcare systems.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.518
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.005
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 head0.093
GPT teacher head0.558
Teacher spread0.465 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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