Teamwork Assessment Tools in Obstetric Emergencies
Bibliographic record
Abstract
INTRODUCTION: Team-based training and simulation can improve patient safety, by improving communication, decision making, and performance of team members. Currently, there is no general consensus on whether or not a specific assessment tool is better adapted to evaluate teamwork in obstetric emergencies. The purpose of this qualitative systematic review was to find the tools available to assess team effectiveness in obstetric emergencies. METHODS: We searched Embase, Medline, PubMed, Web of Science, PsycINFO, CINAHL, and Google Scholar for prospective studies that evaluated nontechnical skills in multidisciplinary teams involving obstetric emergencies. The search included studies from 1944 until January 11, 2016. Data on reliability and validity measures were collected and used for interpretation. A descriptive analysis was performed on the data. RESULTS: Thirteen studies were included in the final qualitative synthesis. All the studies assessed teams in the context of obstetric simulation scenarios, but only six included anesthetists in the simulations. One study evaluated their teamwork tool using just validity measures, five using just reliability measures, and one used both. The most reliable tools identified were the Clinical Teamwork Scale, the Global Assessment of Obstetric Team Performance, and the Global Rating Scale of performance. However, they were still lacking in terms of quality and validity. CONCLUSIONS: More work needs to be conducted to establish the validity of teamwork tools for nontechnical skills, and the development of an ideal tool is warranted. Further studies are required to assess how outcomes, such as performance and patient safety, are influenced when using these 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 | 0.049 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".