Measuring the teamwork performance of teams in crisis situations: a systematic review of assessment tools and their measurement properties
Bibliographic record
Abstract
Background Educational interventions to improve teamwork in crisis situations have proliferated in recent years with substantial variation in teamwork measurement. This systematic review aimed to synthesise available tools and their measurement properties in order to identify the most robust tool for measuring the teamwork performance of teams in crisis situations. Methods Searches were conducted in Embase (via OVID), PsycINFO, Cumulative Index to Nursing and Allied Health Literature, Education Resources Information Center, Medline and Medline In-Process (via OVID) (through 12 January 2017). Studies evaluating the measurement properties of teamwork assessment tools for teams in clinical or simulated crisis situations were included. Two independent reviewers screened studies based on predetermined criteria and completed data extraction. Risk of bias was assessed using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist. Results The search yielded 1822 references. Twenty studies were included, representing 13 assessment tools. Tools were primarily assessed in simulated resuscitation scenarios for emergency department teams. The Team Emergency Assessment Measure (TEAM) had the most validation studies (n=5), which demonstrated three sources of validity (content, construct and concurrent) and three sources of reliability (internal consistency, inter-rater reliability and test–retest reliability). Most studies of TEAM’s measurement properties were at no risk of bias. Conclusions A number of tools are available for assessing teamwork performance of teams in crisis situations. Although selection will ultimately depend on the user’s context, TEAM may be the most promising tool given its measurement evidence. Currently, there is a lack of tools to assess teamwork performance during intraoperative crisis situations. Additional research is needed in this regard.
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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 | 0.044 | 0.194 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| 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".