Preferences of Pregnant Women and Family Members for Maternal-fetal Health States: A Cross-Sectional Study [35N]
Bibliographic record
Abstract
INTRODUCTION: Our objective was to determine whether pregnant women and their family members choose differently when making medical decisions in pregnancy. METHODS: We conducted a cross-sectional study on pregnant women with chronic medical conditions and their family members involved in medical decision making, seen at Mount Sinai Hospital, Toronto. Participants were presented with seven vignettes related to the maternal and fetal outcomes related to the use of anticoagulants in pregnancy. Preferences values were obtained between 0-100 (zero representing death and 100 representing perfect health), using the visual analogue scale (VAS) and the standard gamble (SG). RESULTS: 32 pregnant women, 31 family members, and 32 pairs completed the study. The median age for pregnant women and their family members was 32.5 and 35 years respectively. Most were in partnered relationships, Caucasian, of North American descent, educated, employed with combined annual incomes ≥100 000 Canadian Dollars and risk-averse. While preference values obtained from combined interviews by VAS showed no distinct pattern, those obtained by the SG were closer to those of the family member. . The health-state with the highest preference value was “mother ok, minor fetal malformation” while the lowest involved maternal blood clot with either a major fetal malformation or fetal loss. CONCLUSION: Preference values for maternal-fetal health-states obtained through shared interviews with pregnant women and their family members are closer to those of the family member than the pregnant woman. Reasons for these differences are being explored as part of a qualitative study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".