ArticleBMC health services research2021
Mental health service preferences of patients and providers: a scoping review of conjoint analysis and discrete choice experiments from global public health literature over the last 20 years (1999-2019).
Article in BMC health services research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Consumer Willingness to Share Personal Digital Information for Health-Related Uses.JAMA network open · 2022Trial
- Consumer Preferences for AI-Based Smart Home Medical Emergency Detection Among German Adults: Choice-Based Conjoint Analysis.JMIR AI · 2026Article
- Article
- Preferred conditions for promoting participation in on-site oral health surveys among Japanese adults: insights from a conjoint analysis.BMC public health · 2026Article
- Attributes and levels in discrete choice experiments for pharmacy services: A systematic review to inform asthma intervention design.Exploratory research in clinical and social pharmacy · 2026Review
- Physician Preferences for Universal Routine Depression Screening for Adolescents in Primary Care.JAMA network open · 2025Article
- Construction and Initial Psychometric Validation of the Morana Scale: A Multidimensional Projective Tool Developed Using AI-Generated Illustrations.Journal of clinical medicine · 2025Article
- A Qualitative Evaluation of the Before Operational Stress Program: A Pan-Canadian Study of Mental Health Training for Frontline Public Safety Personnel and Healthcare Providers.Stress and health : journal of the International Society for the Investigation of Stress · 2025Article
- Discrete Choice Experiment Versus Best-Worst Scaling: An Empirical Comparison in Eliciting Young People's Preferences for Web-Based Mental Health Interventions.The patient · 2025Article
- Young People's Preferences for Web-Based Mental Health Interventions for Managing Anxiety and Depression: A Discrete Choice Experiment.Applied health economics and health policy · 2025Article
- A choice based conjoint analysis of mobile healthcare application preferences among physicians, patients, and individuals.NPJ digital medicine · 2025Article
- What drives older adults' acceptance of virtual humans? A conjoint and latent class analysis on virtual exercise coach attributes for a community-based exercise program.Computers in human behavior · 2025Article
- A qualitative study on elderly patients' preferences for inpatient psychiatric services.BMC psychiatry · 2025Article
- Optimizing military mental health and stress resilience training through the lens of trainee preferences: A conjoint analysis approach.Military psychology : the official journal of the Division of Military Psychology, American Psychological Association · 2025Article
- Gender differences in preferences for mental health apps in the general population - a choice-based conjoint analysis from Germany.BMC psychiatry · 2024Article
- Balancing choice and socioeconomic realities: analyzing behavioral and economic factors in social oocyte cryopreservation decisions.Frontiers in endocrinology · 2024Article
- Tele-mental health during the COVID-19 pandemic: A systematic review of the literature focused on technical aspects and challenges.Health science reports · 2023Article
- Availability of screening and treatment for common mental disorders in HIV clinic settings: data from the global International epidemiology Databases to Evaluate AIDS (IeDEA) Consortium, 2016-2017 and 2020.Journal of the International AIDS Society · 2023Article
- Preferences for Health Care and Its Determinants Among Mentally Ill Patients of the West Shoa Zone Community, Oromia, Ethiopia.Patient preference and adherence · 2023Article
- Understanding depression treatment and perinatal service preferences of Kenyan pregnant adolescents: A discrete choice experiment.PloS one · 2023Review
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Abstract
backgroundIn designing, adapting, and integrating mental health interventions, it is pertinent to understand patients' needs and their own perceptions and values in receiving care. Conjoint analysis (CA) and discrete choice experiments (DCEs) are survey-based preference-elicitation approaches that, when applied to healthcare settings, offer opportunities to quantify and rank the healthcare-related choices of patients, providers, and other stakeholders. However, a knowledge gap exists in characterizing the extent to which DCEs/CA have been used in designing mental health services for patients and providers.
methodsWe performed a scoping review from the past 20 years (2009-2019) to identify and describe applications of conjoint analysis and discrete choice experiments. We searched the following electronic databases: Pubmed, CINAHL, PsychInfo, Embase, Cochrane, and Web of Science to identify stakehold,er preferences for mental health services using Mesh terms. Studies were categorized according to pertaining to patients, providers and parents or caregivers.
resultsAmong the 30 studies we reviewed, most were published after 2010 (24/30, 80%), the majority were conducted in the United States (11/30, 37%) or Canada (10/30, 33%), and all were conducted in high-income settings. Studies more frequently elicited preferences from patients or potential patients (21/30, 70%) as opposed to providers. About half of the studies used CA while the others utilized DCEs. Nearly half of the studies sought preferences for mental health services in general (14/30, 47%) while a quarter specifically evaluated preferences for unipolar depression services (8/30, 27%). Most of the studies sought stakeholder preferences for attributes of mental health care and treatment services (17/30, 57%).
conclusionsOverall, preference elicitation approaches have been increasingly applied to mental health services globally in the past 20 years. To date, these methods have been exclusively applied to populations within the field of mental health in high-income countries. Prioritizing patients' needs and preferences is a vital component of patient-centered care - one of the six domains of health care quality. Identifying patient preferences for mental health services may improve quality of care and, ultimately, increase acceptability and uptake of services among patients. Rigorous preference-elicitation approaches should be considered, especially in settings where mental health resources are scarce, to illuminate resource allocation toward preferred service characteristics especially within low-income settings.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.