Parent engagement in perinatal mortality reviews: an online survey of clinicians from six high‐income countries
Bibliographic record
Abstract
OBJECTIVE: Parent engagement in perinatal mortality review meetings following stillbirth may benefit parents and improve patient safety. We investigated perinatal mortality review meeting practices, including the extent of parent engagement, based on self-reports from healthcare professionals from maternity care facilities in six high-income countries. DESIGN: Cross-sectional online survey. SETTING: Australia, Canada, Ireland, New Zealand, UK and USA. POPULATION: A total of 1104 healthcare professionals, comprising mainly obstetricians, gynaecologists, midwives and nurses. METHODS: Data were drawn from responses to a survey covering stillbirth-related topics. Open- and closed-items that focused on 'Data quality on causes of stillbirth' were analysed. MAIN OUTCOME MEASURES: Healthcare professionals' self-reported practices around perinatal mortality review meetings following stillbirth. RESULTS: Most clinicians (81.0%) were aware of regular audit meetings to review stillbirth at their maternity facility, although this was true for only 35.5% of US respondents. For the 854 respondents whose facility held regular meetings, less than a third (31.1%) reported some form of parent engagement, and this was usually in the form of one-way post-meeting feedback. Across all six countries, only 17.1% of respondents described an explicit approach where parents provided input, received feedback and were represented at meetings. CONCLUSIONS: We found no established practice of involving parents in the perinatal mortality review process in six high-income countries. Parent engagement may hold the key to important lessons for stillbirth prevention and care. Further understanding of approaches, barriers and enablers is warranted. TWEETABLE ABSTRACT: Parent engagement in mortality review after stillbirth is rare, based on data from six countries. We need to understand the barriers.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".