Decision aids for people facing health treatment or screening decisions
Notice bibliographique
Résumé
BACKGROUND: Decision aids prepare people to participate in preference-sensitive decisions. OBJECTIVES: 1. Create a comprehensive inventory of patient decision aids focused on healthcare options. 2. Review randomized controlled trials (RCT) of decision aids, for people facing healthcare decisions. SEARCH STRATEGY: Studies were identified through databases and contact with researchers active in the field. SELECTION CRITERIA: Two independent reviewers screened abstracts for interventions designed to aid patients' decision making by providing information about treatment or screening options and their associated outcomes. Information about the decision aids was compiled in an inventory; those that had been evaluated in a RCT were reviewed in detail. DATA COLLECTION AND ANALYSIS: Two reviewers independently extracted data using standardized forms. Results of RCTs were pooled using weighted mean differences (WMD) and relative risks (RR) using a random effects model. MAIN RESULTS: Over 200 decision aids were identified. Of the 131 available decision aids, most are intended for use before counselling. Using the CREDIBLE criteria to evaluate the quality of the decision aids: a) most included potential harms and benefits, credentials of the developers, description of their development process, update policy, and were free of perceived conflict of interest; b) many included reference to relevant literature; c) few included a description of the level of uncertainty regarding the evidence; and d) few were evaluated. Thirty of these decision aids were evaluated in 34 RCTs and another trial evaluated a suite of eight decision aids. An additional 30 trials are yet to be published. Among the trials comparing decision aids to usual care, decision aids performed better in terms of: a) greater knowledge (WMD 19 out of 100, 95% CI: 13 to 24; b) more realistic expectations (RR 1.4, 95%CI: 1.1 to 1.9); c) lower decisional conflict related to feeling informed (WMD -9.1 of 100, 95%CI: -12 to -6); d) increased proportion of people active in decision making (RR 1.4, 95% CI: 1.0 to 2.3); and e) reduced proportion of people who remained undecided post intervention (RR 0.43, 95% CI: 0.3 to 0.7). When simpler were compared to more detailed decision aids, the relative improvement was significant in: a) knowledge (WMD 4 out of 100, 95% CI: 3 to 6); b) more realistic expectations (RR 1.5, 95% CI: 1.3 to 1.7); and c) greater agreement between values and choice. Decision aids appeared to do no better than comparisons in affecting satisfaction with decision making, anxiety, and health outcomes. Decision aids had a variable effect on which healthcare options were selected. REVIEWER'S CONCLUSIONS: The availability of decision aids is expanding with many on the Internet; however few have been evaluated. Trials indicate that decision aids improve knowledge and realistic expectations; enhance active participation in decision making; lower decisional conflict; decrease the proportion of people remaining undecided, and improve agreement between values and choice. The effects on persistence with chosen therapies and cost-effectiveness require further evaluation. Finally, optimal strategies for dissemination need to be explored.
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 | 0,040 | 0,195 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,030 | 0,002 |
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 ».