Development of a discrete choice experiment questionnaire to elicit preferences by pregnant women and policymakers for the expansion of non-invasive prenatal screening
Bibliographic record
Abstract
OBJECTIVE: An instrument for measuring intervention preferences applicable to both patients and policymakers would make it possible to better confront the needs of the supply and demand sides of the health care system. This study aimed to develop a discrete choice experiments (DCE) questionnaire to elicit the preferences of patients and policymakers. The instrument was specifically developed to estimate preferences for new conditions to be added to a screening program for fetal chromosomal anomalies. METHODS: A DCE development study was conducted. The methods employed included a literature review, a qualitative study (based on individual semi-structured interviews, consultations, and a focus group discussion) with pregnant women and policymakers, and a pilot project with 33 pregnant women to validate the first version of the instrument and test the feasibility of its administration. RESULTS: An initial list of 10 attributes was built based on a literature review and the qualitative research components of the study. Five attributes were built based on the responses provided by the participants from both groups. Eight attributes were consensually retained. A pilot project performed on 33 pregnant women led to a final instrument containing seven attributes: 'conditions to be screened', 'test performance', 'moment at gestational age to obtain the test result', 'degree of test result certainty to the severity of the disability', 'test sufficiency', 'information provided from test result', and 'cost related to the test'. CONCLUSION: It is possible to reach a consensus on the construction of a DCE instrument intended to be administered to pregnant women and policymakers. However, complete validation of the consensual instrument is limited because there are too few voting members of health technology assessment agencies committees to statistically ascertain the relevance of the attributes and their levels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".