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Record W4390664442 · doi:10.1080/13561820.2023.2294755

A critical interpretive synthesis of interprofessional education interventions

2024· article· en· W4390664442 on OpenAlexaff
Sanne Kaas-Mason, Sylvia Langlois, Sabrina Bartlett, Farah Friesen, Stella Ng, Daniela Bellicoso, Paula Rowland

Bibliographic record

VenueJournal of Interprofessional Care · 2024
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsThe Wilson CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsInterprofessional educationPsychological interventionTeamworkMedical educationIntervention (counseling)LicensureMedicinePsychologyNursingHealth carePolitical science

Abstract

fetched live from OpenAlex

Interprofessional practice can look quite different depending on a number of dynamics. Interprofessional education interventions may or may not orient toward this range of practice possibilities. This literature review explores: (1) how interprofessional education interventions relate to different kinds of interprofessional practice and (2) the range of interprofessional practices assumed by interprofessional education interventions. Four databases were searched for articles published between 2011-2021 describing pre-licensure level interprofessional education interventions, resulting in a dataset of 110 articles. Our analysis involved (1) descriptive summaries of the articles, and (2) content analysis of the rationale and description of the intervention. Of the articles, 93% (102/110) of interprofessional education interventions were designed and/or evaluated using the concept of interprofessional education competencies. "Teamwork" was the most relied upon competency. Most articles were not explicit about the different kinds of interprofessional practices that these competencies might be oriented toward. Our study substantiates earlier claims that interprofessional education literature tends to focus on competencies and orient toward undifferentiated understandings of "teamwork." This analysis is particularly important as interprofessional teams are engaging in increasingly complex, fluid, and distributed forms of interprofessional practice that may not be captured in an undifferentiated approach to "teamwork."

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.025
GPT teacher head0.499
Teacher spread0.474 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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