Impact of introducing Practical Obstetric Multi‐Professional Training (<scp>PROMPT</scp>) into maternity units in Victoria, Australia
Bibliographic record
Abstract
OBJECTIVE: To assess the introduction of Practical Obstetric Multi-professional Training (PROMPT) into maternity units and evaluate effects on organisational culture and perinatal outcomes. DESIGN: A retrospective cohort study. SETTING: Maternity units in eight public hospitals in metropolitan and regional Victoria, Australia. POPULATION: Staff in eight maternity units and a total of 43,408 babies born between July 2008 and December 2011. METHODS: Representatives from eight Victorian hospitals underwent a single day of training (Train the Trainer), to conduct PROMPT. Organisational culture was compared before and after PROMPT. Clinical outcomes were evaluated before, during and after PROMPT. MAIN OUTCOME MEASURES: The number of courses run and the proportion of staff trained were determined. Organisational culture was measured using the Safety Attitude Questionnaire. Clinical measures included Apgar scores at 1 and 5 minutes (Apgar 1 and Apgar 5), cord lactate, blood loss and length of baby's stay in hospital. RESULTS: Seven of the eight hospitals conducted PROMPT. Overall about 50% of staff were trained in each year of the study. Significant increases were found in Safety Attitude Questionnaire scores representing domains of teamwork (Hedges' g 0.27, 95% confidence interval [95% CI] 0.13-0.41), safety (Hedges' g 0.28, 95% CI 0.15-0.42) and perception of management (Hedges' g 0.17, 95% CI 0.04-0.31). There were significant improvements in Apgar 1 (OR 0.84, 95% CI 0.77-0.91), cord lactates (odds ratio 0.92, 95% CI 0.85-0.99) and average length of baby's stay in hospital (Hedges' g 0.03, 95% CI 0.01-0.05) during or after training, but no change in Apgar 5 scores or proportion of cases with high blood loss. CONCLUSION: PROMPT can be introduced using the Train the Trainer model. Improvements in organisational culture and some clinical measures were observed following PROMPT.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".