High-performance team assessment instruments: A scoping review
Bibliographic record
Abstract
Background & Aim: Assessing team performance is crucial in developing effective management strategies within healthcare. Therefore, identifying reliable tools that accurately measure team competencies is essential. This study aims to review the existing evidence on valid instruments to evaluate high-performance teams in healthcare. Methods & Materials: A scoping review was conducted according to the methodology proposed by the Joanna Briggs Institute. The study was based on the PCC framework (Population, Concept, and Context), concentrating on healthcare teams (population), tools for evaluating high-performance teams (concept), and all areas of professional practice (context). The literature search included databases such as CINAHL Complete (EBSCO), LILACS (BVS), MEDLINE Complete (PubMed), PsycINFO (EBSCO), and Scopus. Grey literature was searched on WorldCat and ProQuest Dissertations & Theses. Study selection took place in two stages: an initial screening of titles and abstracts to identify relevant studies, followed by a full-text review of the selected articles. Results: Initially identified 1,104 articles and selected six for inclusion in this review. Four instruments were recognized for assessing high-performance teams in healthcare: the Mayo High Performance Teamwork Scale, the Ottawa Crisis Resource Management Global Rating Scale, the Trust, and the Team Performance Observation Tool. Conclusion: The primary contribution of this study was the identification of tools that provide constructive feedback to facilitate the ongoing development of high-performance teams. The implementation of systematic, evidence-based strategies supported by these evaluation tools fosters a data-driven approach to clinical decision-making and enhances the overall quality of care
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 | 0.090 | 0.258 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.054 | 0.048 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".