A Discrete Choice Experiment on Women’s Preferences for Water Immersion During Labor and Birth: Identification, Refinement and Selection of Attributes and Levels
Bibliographic record
Abstract
OBJECTIVES: To identify attributes (i.e., characteristics describing a scenario) and levels (i.e., each characteristic may be defined by a different level) that would be included in a discrete choice experiment (DCE) questionnaire to evaluate women's preferences for water immersion during labor and birth. METHODS: A mixed-method approach, combining systematic reviews of the literature and patient focus groups to identify attributes and levels explaining women's preferences. After the focus groups, preference exercises were conducted and led to the creation of the questionnaire, including the DCE. A qualitative validation of the questionnaire was conducted with women from the focus groups and with medical experts. RESULTS: The literature reviews provided 26 attributes to be considered for childbirth in water, and focus groups identified 14 additional attributes. From these 40 attributes, preference exercises allowed us to select four for the DCE, in addition to the birth mode. Labor duration was also included, even if it was not well ranked, as it is the main clinical outcome in the literature. Validation with experts and women did not change the choice of attributes but slightly changed the levels selected. The final six attributes were: birth mode, duration of the labor phase, pain sensation, risk of severe tears in the perineum during the expulsion of the newborn, risk of death of the newborn, and general condition of the newborn (Apgar) score at 5 minutes. CONCLUSION: This study allowed us to detail all the stages for the design of a DCE questionnaire. To date, this is the first study of this kind in the context of women's preferences for water immersion during labor and birth.
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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 | 0.027 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".