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Record W4406407202 · doi:10.1080/13561820.2025.2451957

Cross-cultural adaptation and evidence of validity of the interprofessional collaboration scale (IPC-BR) for Brazil

2025· article· en· W4406407202 on OpenAlexaff
Jaira Gonçalves Trigueiro, Marcelo Viana da Costa, Márcio Adriano Fernandes Barreto, Merrick Zwarenstein, Rhanna Emanuela Fontenele Lima de Carvalho

Bibliographic record

VenueJournal of Interprofessional Care · 2025
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsWestern University
Fundersnot available
KeywordsAdaptation (eye)Scale (ratio)PsychologyMedical educationNursingApplied psychologyMedicineGeography

Abstract

fetched live from OpenAlex

We aimed to perform cross-cultural adaptation of the Interprofessional Collaboration Scale (IPC-BR) and to evaluate evidence of its validity for the Brazilian hospital context. The research consisted of six steps: translation of the instrument into the new language, synthesis of the translated versions, back-translation, synthesis of the versions in the original language, evaluation of the syntheses by an expert committee, and pilot testing or pretesting and validation of the internal structure of the items of the instrument. The pilot testing involved 4 translators, 14 judges, and 30 healthcare professionals; the validation of the internal structure involved 686 professionals including nurses, physicians and physiotherapists. Translation and cross-cultural adaptation revealed no significant changes or discrepancies in meaning from the original model. Exploratory, confirmatory, and parallel factor analyses confirmed that the Brazilian scale is unidimensional. We found unidimensional characteristics and satisfactory factor loadings, with good levels of reliability, which makes the instrument provide consistent and reliable internal evidence for measuring the construct. Thus, the possibility of using it to assess interprofessional collaboration among different target groups in the Brazilian scenario was confirmed.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.057
GPT teacher head0.504
Teacher spread0.447 · 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.

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

Citations4
Published2025
Admission routes1
Has abstractyes

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