Eliciting individual preferences for health care: a case study of perinatal care
Bibliographic record
Abstract
OBJECTIVE: To demonstrate how a discrete choice experiment (DCE) can be used to elicit individuals' preferences for health care and how these preferences can be incorporated into a cost-benefit analysis. METHODS: A DCE which elicited preferences for three perinatal services: specialist nurse appointments; home visits from a trained lay visitor; and home-help. Cost was included to obtain a monetary measure of the value that individuals place on the services. In total, 292 women who had previously participated in a randomized trial of alternative forms of pre-natal care were interviewed. RESULTS: The most preferred service configuration consisted of three nurse appointments and two home visits before birth and 4 h of home-help per week for the first 4 weeks after birth. On average, women are willing to pay $371 for this package. A package that excluded home-help was valued at $122 whilst provision of three nurse appointments only was valued at $97. The predicted uptake of the services ranged from 37% to 93% depending on the woman's experience with the service, whether or not it was her first child and her level of education. CONCLUSION: The willingness to pay values were much higher than the costs for nurse appointments, suggesting this service produces a net social benefit. The willingness to pay for the package including both the nurse appointments and home visits only just exceeded the costs of the package, suggesting there is a relatively high chance that this package produces a net social loss.
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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".