Interdisciplinary Health Care Evaluation Instruments: A Review of Psychometric Evidence
Bibliographic record
Abstract
Teamwork among health care professionals has been found to improve patient outcomes and reduce burnout. Surveys from individual team members are often used to measure the effectiveness of teamwork performance, as they provide an efficient way to capture various constructs of teamwork. This allows evaluators to better understand team functioning, areas of strength, and to identify potential areas for improvement. However, the majority of published surveys are yet to be validated. We conducted a review of psychometric evidence to identify instruments frequently used in practice and identified in the literature. The databases searched included MEDLINE, EMBASE, CINAHL, and PsycINFO. After excluding duplicates and irrelevant articles, 15 articles met the inclusion criteria for full assessment. Seven surveys were validated and most frequently identified in the literature. This review aims to facilitate the selection of instruments that are most appropriate for research and clinical practice. More research is required to develop surveys that better reflect the current reality of teamwork in our evolving health system, including a greater consideration for patient as team members. Additionally, more research is needed to encompass an increasing development of team assessment tools.
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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 | 0.042 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".