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Record W4414531761 · doi:10.1080/13561820.2025.2562066

Interprofessional tensions on designing an arts-based theatre intervention in a rehabilitation setting

2025· article· en· W4414531761 on OpenAlexafffund
Sébastien Finlay, Camille D’Anjou, Stefano Rezzonico, Maud Gendron-Langevin, Guylaine Le Dorze, Ingrid Verduyckt

Bibliographic record

VenueJournal of Interprofessional Care · 2025
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversité du Québec à MontréalUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre for Interdisciplinary Research in Rehabilitation
FundersCentre for Interdisciplinary Research in Rehabilitation
KeywordsIntervention (counseling)RehabilitationFocus groupQualitative researchPopulationInterprofessional educationKey (lock)

Abstract

fetched live from OpenAlex

This study examines the tensions encountered by an interprofessional team collaborating to design an arts-based theater intervention in a rehabilitation setting. A qualitative focus group with 13 participants, including clinicians, patient participants, researchers, and theater professionals, identified five key tensions: defining the intervention (physicality vs. intellectualization), roles of clinicians (defined vs. transversal), objectives of the intervention (therapeutic vs. theatrical), modality of the intervention (individual vs. group), and target population (specific vs. general). These findings underscore the challenge of balancing disciplinary expertise with innovation in collaborative settings. Reflective practices can help navigate these tensions, fostering integration of creative and clinical approaches. Future research should examine how such dynamics shape collaboration across diverse contexts.

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)0.128
metaresearch head score (Gemma)0.158
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.679

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1280.158
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0200.018
Scholarly communication0.0130.009
Open science0.0040.023
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0030.001

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 head0.030
GPT teacher head0.480
Teacher spread0.450 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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