DEVELOPING THE CHINESE VERSION OF THE ASSESSMENT OF INTERPROFESSIONAL TEAM COLLABORATION SCALE IN TAIWAN
Bibliographic record
Abstract
Abstract The delivery of long-term care reablement services emphasizes an interprofessional collaboration (IPC) approach. This study aims to use the existing Assessment of Interprofessional Team Collaboration Scale (AITCS) as an IPC measurement tool, assessing its alignment with Taiwan’s unique caring culture compared to its original use in Canada. Brislin’s Back-Translation Method was employed to create Chinese versions of the AITCS questionnaires, which were then administered to 201 professionals and paraprofessionals involved in reablement services in Taiwan to evaluate reliability and validity. Consistent with the original AITCS, the Chinese Version scale comprises three primary domains—partnership, cooperation, and coordination—with 23 question items. It demonstrates satisfactory reliability (Cronbach’s alpha=0.97 for total scale, ICC=0.91 for test-retest reliability) and acceptable convergent validity (AVE=0.68-0.80, CR>0.90), along with discriminant validity (coefficients < 0.80 between domains). Analysis of IPC performance among participants shows that collaboration (3.82 ± 0.75) and partnership (3.78 ± 0.77) exhibit more favorable scales compared to cooperation (3.56 ± 0.63) (p<.001). This study has preliminarily developed a Chinese Version of the IPC measurement tool, suggesting the need for further investigation into strategies for enhancing cooperation in IPC performance within the context of reablement services in Taiwan.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".