Validation and reliability assessment of the Persian Adaptation of the Interprofessional Team Collaboration Scale II (P-AITCS-II) for Iranian healthcare providers
Bibliographic record
Abstract
The primary objective of this study was to perform a psychometric evaluation of the Persian adaptation of the Assessment of Interprofessional Team Collaboration Scale (P-AITCS-II). This methodological study aimed to assess the validity and reliability of the AITCS-II for practitioners within the Iranian healthcare context. Data were collected from a sample of 230 Iranian healthcare providers between May and June 2024. Confirmatory factor analysis demonstrated good model fit indices (χ2 = 540.20, df = 224, χ2/df = 2.41, CFI = 0.917, IFI = 0.918, TLI = 0.907, PNFI = 0.768, PCFI = 0.812, and RMSEA = 0.079 [CI90% 0.070-0.087]). These results confirm the validity of the P-AITCS-II model. Additionally, the internal consistency and composite reliability of the three factors were higher than 0.7. Convergent validity was considered acceptable for the P-AITCS-II, as the Average Variance Extracted (AVE) was greater than 0.5. The Persian adaptation of the Assessment of Interprofessional Team Collaboration Scale II (P-AITC-II), consisting of 23 items within three factors-partnership, cooperation, and coordination-demonstrated good validity and reliability. However, further research is needed to confirm its robustness and usefulness for improving interprofessional team collaboration.
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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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".