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 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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".