Assessing the Societal Value of Preventing Fetal Deaths by Using a Households Survey in the United States
Bibliographic record
Abstract
Many studies have found that the value of a life saved varies based on its characteristics, such as age. However, no study has investigated the value of avoiding fetal deaths, which represent a substantial disease burden worldwide. To evaluate this, 2, 607 adults in the USA were surveyed online and asked to allocate a unique life-saving treatment between an unborn child, a newborn infant and a 5-year-old child. The majority (69%) of respondents preferred to allocate the treatment to a newborn infant over an unborn child in the sixth month of pregnancy, 5% chose the unborn child and 26% could not decide, preferring to leave the outcome to chance. Similarly, 54% chose a newborn infant over an unborn child in the ninth month of pregnancy, 39% could not decide and 7% chose the unborn child. Approximately 75% of respondents who chose the newborn found the decision difficult. The strengths of preferences for unborn children were between 46% and 56% of the level for newborns. Preferences varied significantly by income, religious inclination, intent to have a child, previous experience of fetal loss, occupation and gender. Based on the survey results, society puts value on avoiding fetal loss, albeit less than on preventing the death of a newborn child.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".