Counseling parents at risk of delivery of an extremely premature infant: Differing strategies
Bibliographic record
Abstract
BACKGROUND: It is not known how neonatologists address the affective and cognitive loads on parents deciding whether to resuscitate infants born extremely preterm. This study explores expert neonatologists' views on these decision-making processes and their own roles in counseling parents. METHODS: Semistructured interviews asked internationally recognized experts to share their perspectives on perinatal consultations. Their responses were subjected to thematic analysis. RESULTS: Eighteen of 22 invited experts participated. Approximately equal numbers reported employing a physician-driven approach, a parent-driven approach, and a combined approach during these consultations. Those who followed a physician-driven approach typically focused on conveying standard information about adverse outcomes. Those who followed a parent-driven approach typically focused on addressing parents' information requests, guiding their decision making, and providing affective support. Nearly all experts, in each group, endorsed addressing the child's quality of life, in terms of functionality, when discussing long-term outcomes. Although many believed that families adjusted to life with a disabled child, few discussed the topic during prenatal consultations. Most, in each group, reported trying to alleviate future "decisional regret" for parents whose premature infants subsequently became disabled. None spoke to parents about possible decisional regret after deciding to forgo resuscitation. CONCLUSIONS: Expert neonatologists are deeply concerned that parents understand the decision facing them. However, they differ on what information they offer and how they balance parents' need for cognitive and affective support. They expressed more concern about parents' decisional regret should their child survive resuscitation, but have severe disability, than about decisional regret after foregoing resuscitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".