Eliciting women's preferences in health care: A review of the literature
Bibliographic record
Abstract
OBJECTIVES: The increasing availability of information about health care suggests an expanding role for consumers to exercise their preferences in health-care decision-making. Numerous methods are available to assess consumer preferences in health care. We conducted a systematic review to characterize the study of women's preferences about health care. METHODS: A MEDLINE search from 1965 to July 1999 was conducted as well as hand searches of the Medical Decision Making Journal (1981-1999) and references from retrieved articles. Only original articles on women's health issues were selected. Information on thirty-one variables related to study characteristics and preferences were extracted by two independent investigators. A third investigator resolved disagreements. Qualitative and quantitative analyses were conducted to synthesize the data. RESULTS: Four hundred eighty-three studies were identified in the initial search. Seventy articles were selected for review based on title, abstract, and inclusion criteria. There was an increase in published articles and number of methods used to elicit preferences. White women were studied more than black women (p < .001). Preferences were mainly studied in outpatient settings (p < .005) and in the United States, United Kingdom, and Canada (83 percent). Preferences related to participation in decision-making were the most common (21 percent). Only 4 percent of the studies were performed to inform the debate for public policy questions. Willingness to pay was the method most used (11 percent), followed by category scaling (10 percent), rating scale (9 percent), standard-gamble (6 percent). Preferences for individual particular (opposed to sequential and health states) outcomes (68 percent), different treatments/tests (47 percent), and related to a treatment episode (31 percent) were addressed. Information regarding diseases, conditions, or procedures was given in 57 percent of studies. Information provided was mainly written (37 percent) and included positive and negative potential outcomes (67 percent). There is no relationship between the method or tool used for delivery information and the choice performed. CONCLUSIONS: The literature on preferences in women's health care is limited to a fairly homogeneous population (white women from the United States, United Kingdom, and Canada). Additionally, use of utility-based measures to capture preferences has decreased over time while others methods (e.g., time trade-off [TTO], contingent valuation) have increased. Women's preferences are not necessarily uniform even when asked similar questions using similar tools. Little information on women's preferences exists to inform policy-makers about women's health care.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".