Evaluating Teamwork in a Simulated Obstetric Environment
Bibliographic record
Abstract
BACKGROUND: The National Confidential Enquiry into Maternal Deaths identified "lack of communication and teamwork" as a leading cause of substandard obstetric care. The authors used high-fidelity simulation to present obstetric scenarios for team assessment. METHODS: Obstetric nurses, physicians, and resident physicians were repeatedly assigned to teams of five or six, each team managing one of four scenarios. Each person participated in two or three scenarios with differently constructed teams. Participants and nine external raters rated the teams' performances using a Human Factors Rating Scale (HFRS) and a Global Rating Scale (GRS). Interrater reliability was determined using intraclass correlations and the Cronbach alpha. Analyses of variance were used to determine the reliability of the two measures, and effects of both scenario and rater profession (R.N. vs. M.D.) on scores. Pearson product-moment correlations were used to compare external with self-generated assessments. RESULTS: The average of nine external rater scores showed good reliability for both HFRS and GRS; however, the intraclass correlation coefficients for a single rater was low. There was some effect of rater profession on self-generated HFRS but not on GRS. An analysis of profession-specific subscores on the HFRS revealed no interaction between profession of rater and profession being rated. There was low correlation between externally and self-generated team assessments. CONCLUSIONS: This study does not support the use of the HFRS for assessment of obstetric teams. The GRS shows promise as a summative but not a formative assessment tool. It is necessary to develop a domain specific behavioral marking system for obstetric teams.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".