Determination of the psychometric properties of a behavioural marking system for obstetrical team training using high-fidelity simulation: Table 1
Bibliographic record
Abstract
BACKGROUND: To determine the effectiveness of high-fidelity simulation for team training, a valid and reliable tool is required. This study investigated the internal consistency, inter-rater reliability and test-retest reliability of two newly developed tools to assess obstetrical team performance. METHODS: After research ethics board approval, multidisciplinary obstetrical teams participated in three sessions separated by 5-9 months and managed four high-fidelity simulation scenarios. Two tools, an 18-item Assessment of Obstetric Team Performance (AOTP) and a six-item Global Assessment of Obstetric Team Performance (GAOTP) were used.(5) Eight reviewers rated the DVDs of all teams' performances. RESULTS: Two AOTP items were consistently incomplete and omitted from the analyses. Cronbach's α for the 16-item AOTP was 0.96, and 0.91 for the six-item GAOTP. The eight-rater α for the GAOTP was 0.81 (single-rater intra-class correlation coefficient, 0.34) indicating acceptable inter-rater reliability. The 'four-scenario' α for the 12 teams was 0.79 for session 1, 0.88 for session 2, and 0.86 for session 3, suggesting that performance is not being strongly affected by the context specificity of the cases. Pearson's correlation of team performance scores for the four scenarios were 0.59, 0.35, 0.40 and 0.33, and for the total score across scenarios it was 0.47, indicating moderate test-retest reliability. CONCLUSIONS: The results from this study indicate that the GAOTP would be a sufficient assessment tool for obstetrical team performance using simulation provided that it is used to assess teams with at least eight raters to ensure a sufficiently stable score. This could allow the quantitative evaluation of an educational intervention.
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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 | 0.024 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".