Development and usability of a behavioural marking system for performance assessment of obstetrical teams
Bibliographic record
Abstract
BACKGROUND: Teamwork and communication have been identified as root causes of sentinel events involving infant death and injury during delivery. However, despite the emphasis on team training as a way to improve maternal and fetal safety outcomes, valid and reliable markers of obstetrical team performance are not available to assess curricular efficacy. OBJECTIVES: The objective of this study was to develop and assess the usability of two obstetrical behavioural marking systems for use with simulation entitled Assessment of Obstetrical Team Performance (AOTP) and Global Assessment of Obstetrical Team Performance (GAOTP). METHODS: In a previous study, obstetrical teams were videotaped managing simulated emergency obstetrical scenarios. In the current study, 13 reviewers reviewed these videotapes and generated a list of behaviours judged to negatively or positively affect the teams' performances. Qualitative analysis using research team consensus and NVivo generated themes and subthemes. Research team members developed descriptors for poor and excellent team performance for each of the behaviours. Subsequently, the usability of the prototypes was assessed by an additional 14 reviewers. RESULTS: In total, the reviewers identified 1294 items, which were sorted into 6 themes and 18 subthemes of obstetrical team performance. In terms of usability, the median amount of time that participants spent completing the AOTP was 7.5 min (range 1.5 to 50 min) and 75% thought the time requirement was moderate and manageable. CONCLUSION: Feedback regarding usability suggests that the AOTP allows for an accurate reflection of raters' assessments of the performance of the team, and as a whole, it is comprehensive, quick and easy to use. Studies are underway to establish the validity and reliability of the AOTP and GAOTP.
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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 | 0.064 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".