Eliciting women's preferences in health care: A review of the literature
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,003 |
| Bibliométrie | 0,011 | 0,015 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».