Assessment of Interprofessional Team Collaboration Scale (AITCS): Development and Testing of the Instrument
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it