Assessment of Interprofessional Team Collaboration Scale (AITCS): Development and Testing of the Instrument
Bibliographic record
Abstract
INTRODUCTION: Many health professionals believe they practice collaboratively. Providing insight into their actual level of collaboration requires a means to assess practice within health settings. This chapter reports on the development, testing, and refinement process for the Assessment of Interprofessional Team Collaboration Scale (AITCS). There is a paucity of literature and measurement tools addressing interprofessional collaborative team performance and the nature of effective teamwork processes and patient roles within collaborative teams. These gaps limit our knowledge about how health care teams form and function. Instruments are therefore needed to assess collaborative relationships. METHODS: The AITCS, with its 47 items within 4 subscales (partnership, cooperation, coordination, and shared decision making) and assessed on a 5-point Likert scale, was administered to a total of 125 practitioners from 7 health care teams practicing within a variety of settings, in 2 provinces in Canada. RESULTS: Principal components and factor analysis of data resulted in 37 items loading onto 3 factors, explaining 61.02% of the variance. The internal consistency estimates for reliability of each subscale ranged from 0.80 to 0.97, with an overall reliability of 0.98. Thus, the AITCS is a reliable and valid instrument. DISCUSSION: The psychometric analysis of this instrument supports its value in measuring collaboration within teams and when patients are included as team members. The AITCS can be applied to continuing professional education interventions to determine change over time. It has limitations to the Canadian context and within the settings where participants practiced. Further test and retest reliability and longitudinal study application is needed.
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 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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| 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".