Cross-cultural adaptation and evidence of validity of the interprofessional collaboration scale (IPC-BR) for Brazil
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".