Preferences for a third‐trimester ultrasound scan in a low‐risk obstetric population: a discrete choice experiment
Bibliographic record
Abstract
OBJECTIVE: Establish maternal preferences for a third-trimester ultrasound scan in a healthy, low-risk pregnant population. DESIGN: Cross-sectional study incorporating a discrete choice experiment. SETTING: A large, urban maternity hospital in Northern Ireland. PARTICIPANTS: One hundred and forty-six women in their second trimester of pregnancy. METHODS: A discrete choice experiment was designed to elicit preferences for four attributes of a third-trimester ultrasound scan: health-care professional conducting the scan, detection rate for abnormal foetal growth, provision of non-medical information, cost. Additional data collected included age, marital status, socio-economic status, obstetric history, pregnancy-specific stress levels, perceived health and whether pregnancy was planned. Analysis was undertaken using a mixed logit model with interaction effects. MAIN OUTCOME MEASURES: Women's preferences for, and trade-offs between, the attributes of a hypothetical scan and indirect willingness-to-pay estimates. RESULTS: Women had significant positive preference for higher rate of detection, lower cost and provision of non-medical information, with no significant value placed on scan operator. Interaction effects revealed subgroups that valued the scan most: women experiencing their first pregnancy, women reporting higher levels of stress, an adverse obstetric history and older women. CONCLUSIONS: Women were able to trade on aspects of care and place relative importance on clinical, non-clinical outcomes and processes of service delivery, thus highlighting the potential of using health utilities in the development of services from a clinical, economic and social perspective. Specifically, maternal preferences exhibited provide valuable information for designing a randomized trial of effectiveness and insight for clinical and policy decision makers to inform woman-centred care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".