What women want: women's preferences of caregiver behavior when prenatal sonography findings are abnormal
Bibliographic record
Abstract
OBJECTIVE: To determine what women value when receiving news of a pregnancy abnormality detected by ultrasound. METHODS: Women who had a pregnancy complication detected sonographically in the year 2000 were asked to complete a survey of 21 questions measuring the importance of various factors related to the receipt of bad news. Of the target sample of 117 women who agreed to participate, 76 (64.9%) returned completed surveys. Cases included serious anomalies (67%) and soft markers/obstetric complications (33%). RESULTS: Responses to questions on 'information quality', 'prompt provision of information', 'information-provider behavior' and 'information provision environment' showed that women attached the most importance to information quality, much more so than to promptness. Speed was even less important than information-provider empathy. Answers concerning use of the terms 'fetus' or 'baby' revealed greater variation in preferences than any other. Privacy was the most important environmental variable, more important than some information quality variables, or any promptness variable. Intervening variables considered included demographic variables and the seriousness of the prognosis. Education was the most useful predictor of preferences, with highly educated women generally placing less value on environment and some information quality variables, and having different preferences concerning the terms 'fetus' and 'baby'. CONCLUSIONS: Our findings shed some light on what is important to women who face bad news. Although more research is needed in this important area, we hope that our findings may assist institutions and caregivers in establishing guidelines for the effective and considerate communication of bad news.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".