Preliminary testing of the Swedish version of the Assessment of Interprofessional Team Collaboration Scale (AITCS-S)
Bibliographic record
Abstract
Interprofessional collaboration might improve healthcare processes and outcomes; however, it has been found that most instruments that aim to measure collaboration have undergone limited testing. The assessment of interprofessional team collaboration scale (AITCS) is one questionnaire that aims to evaluate collaboration, but it has not yet been extensively tested. The aim of this study was to translate and to cross-culturally adapt the AITCS for use in Sweden, to describe floor and ceiling values, and to investigate the AITCS in terms of reliability, face, and content validity. The study included a total of 349 participants working in team-based pain rehabilitation. The participants were asked to fill in the Swedish version of the AITCS (AITCS-S) at baseline. Of these, 73 participants also completed the AITCS-S two weeks later. The results showed that the content and face validity were good. Internal consistency varied from 0.79 to 0.96 and judged to be acceptable to excellent. Test-retest stability showed excellent stability with intraclass correlation values above 0.75 for all subscales. This study concludes that the Swedish version of the AITCS is a reliable and valid questionnaire. Further psychometric investigations might be undertaken in order to attempt to develop shorter versions of the AITCS-S.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".