Effect of an Educational Presentation about Extremely Preterm Infants on Knowledge and Attitudes of Health Care Providers
Bibliographic record
Abstract
Objective To determine healthcare providers' knowledge (HCP) about survival rates of extremely preterm infants (EPI) and attitudes toward resuscitation before and after an educational presentation and, to examine the relationship between knowledge and attitudes toward resuscitation. Study Design Participants completed a survey before and after attending a presentation detailing evidence-based estimates of survival rates and surrounding ethical issues. Respondents included neonatologists, obstetricians, pediatricians, maternal-fetal medicine specialists, trainees in pediatrics, obstetrics, neonatal-perinatal medicine and neonatal and obstetrical nurses. Results In total, 166 participants attended an educational presentation and 130 participants completed both pre- and postsurveys (response rate 78%). Prepresentation, for all gestations, ≤ 50% of respondents correctly identified survival/intact survival rates. Postpresentation, correct responses regarding survival/intact survival rates ranged from 49 to 86% (p < 0.001) and attitudes shifted toward being more likely to resuscitate at all gestations regardless of parental wishes. There was a weak-to-modest relationship (Spearman's coefficient 0.24–0.40, p < 0.001–0.004) between knowledge responses and attitudes. Conclusion Attendance at an educational presentation did improve HCP knowledge about survival and long term outcomes for EPI, but HCP still underestimated survival and were not always willing to resuscitate in accordance with parental wishes. These findings may represent barriers to some experts' recommendation to use shared decision-making with parents when considering the resuscitation options for their EPI.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".