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Record W1987837922 · doi:10.3109/13561820.2010.505350

Relationships of power: implications for interprofessional education

2010· article· en· W1987837922 on OpenAlexaff
Lindsay Baker, Eileen Egan‐Lee, Maria Athina Martimianakis, Scott Reeves

Bibliographic record

VenueJournal of Interprofessional Care · 2010
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsInterprofessional educationClosure (psychology)Context (archaeology)Power (physics)Health carePerceptionHealth professionalsPsychologySocial careRestructuringMedical educationInclusion (mineral)NursingMedicineSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Interprofessional education (IPE) is considered a key mechanism in enhancing communication and practice among health care providers, optimizing participation in clinical decision making and improving the delivery of care. An important, though under-explored, factor connected to this form of education is the unequal power relations that exist between the health and the social care professions. Drawing on data from the evaluation of a large multi-site IPE initiative, we use Witz's model of professional closure (1992) to explore the perspectives and the experiences of participants and the power relations between them. A subset of interviews with a range of different professionals (n = 25) were inductively analyzed to generate emerging themes related to perceptions of professional closure and power. Findings from this work highlight how professionals' views of interprofessional interactions, behaviours and attitudes tend to either reinforce or attempt to restructure traditional power relationships within the context of an IPE initiative.

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)0.036
metaresearch head score (Gemma)0.064
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: none
Teacher disagreement score0.036
Threshold uncertainty score0.192

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.064
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.007
Science and technology studies0.0190.068
Scholarly communication0.0190.037
Open science0.0050.018
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0100.001

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 head0.033
GPT teacher head0.472
Teacher spread0.439 · 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

Citations358
Published2010
Admission routes1
Has abstractyes

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