Preferences for Early Intervention Mental Health Services: A Discrete-Choice Conjoint Experiment

被引:23
|
作者
Becker, Mackenzie P. E. [1 ]
Christensen, Bruce K. [6 ]
Cunningham, Charles E. [2 ,4 ]
Furimsky, Ivana [2 ,3 ]
Rimas, Heather [2 ]
Wilson, Fiona [2 ,3 ]
Jeffs, Lisa [3 ]
Bieling, Peter J. [2 ,3 ]
Madsen, Victoria [3 ]
Chen, Yvonne Y. S. [2 ,5 ]
Mielko, Stephanie [2 ]
Zipursky, Robert B. [2 ,3 ]
机构
[1] McMaster Univ, Dept Psychol Neurosci & Behav, Hamilton, ON, Canada
[2] McMaster Univ, Dept Psychiat & Behav Neurosci, Hamilton, ON, Canada
[3] St Josephs Healthcare Hamilton, Mental Hlth & Addict Program, Hamilton, ON, Canada
[4] McMaster Childrens Hosp, Dept Psychiat, Hamilton, ON, Canada
[5] Univ Alberta, Sch Business, Edmonton, AB, Canada
[6] Australian Natl Univ, Res Sch Psychol, Canberra, ACT, Australia
关键词
DISORDERS; IMPACT; ONSET; CARE;
D O I
10.1176/appi.ps.201400306
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Objective: Early intervention services (EISs) for mental illness may improve outcomes, although treatment engagement is often a problem. Incorporating patients' preferences in the design of interventions improves engagement. A discrete-choice conjoint experiment was conducted in Canada to identify EIS attributes that encourage treatment initiation. Methods: Sixteen four-level attributes were formalized into a conjoint survey, completed by patients, family members, and mental health professionals (N=562). Participants were asked which EIS option people with mental illness would contact. Latent-class analysis identified respondent classes characterized by shared preferences. Randomized first-choice simulations predicted which hypothetical options, based on attributes, would result in maximum utilization. Results: Participants in the conventional-service class (N=241, 43%) predicted that individuals would contact traditional services (for example, hospital location and staffed by psychologists or psychiatrists). Membership was associated with being a patient or family member and being male. Participants in the convenient-service class (N=321, 57%) predicted that people would contact services promoting easy access (for example, self-referral and access from home). Membership was associated with being a professional. Both classes predicted that people would contact services that included short wait times, direct contact with professionals, patient autonomy, and psychological treatment information. The convenient-service class predicted that people would use an e-health model, whereas the conventional-service class predicted that people would use a primary care or clinic-hospital model. Conclusions: Provision of a range of services may maximize EIS use. Professionals may be more apt to adopt EISs in line with their beliefs regarding patient preferences. Considering several perspectives is important for service design.
引用
收藏
页码:184 / 191
页数:8
相关论文
共 50 条
  • [31] Chronic pain patients' treatment preferences: a discrete-choice experiment
    Muehlbacher, Axel C.
    Junker, Uwe
    Juhnke, Christin
    Stemmler, Edgar
    Kohlmann, Thomas
    Leverkus, Friedhelm
    Nuebling, Matthias
    EUROPEAN JOURNAL OF HEALTH ECONOMICS, 2015, 16 (06): : 613 - 628
  • [32] Preferences for the delivery of early abortion services in Australia: a discrete choice experiment
    Church, Jody
    Haas, Marion
    Street, Deborah J.
    Bateson, Deborah
    Mazza, Danielle
    SEXUAL HEALTH, 2024, 21 (06)
  • [33] Modeling the Decision of Mental Health Providers to Implement Evidence-Based Children’s Mental Health Services: A Discrete Choice Conjoint Experiment
    Charles E. Cunningham
    Melanie Barwick
    Heather Rimas
    Stephanie Mielko
    Raluca Barac
    Administration and Policy in Mental Health and Mental Health Services Research, 2018, 45 : 302 - 317
  • [34] Modeling the Decision of Mental Health Providers to Implement Evidence-Based Children's Mental Health Services: A Discrete Choice Conjoint Experiment
    Cunningham, Charles E.
    Barwick, Melanie
    Rimas, Heather
    Mielko, Stephanie
    Barac, Raluca
    ADMINISTRATION AND POLICY IN MENTAL HEALTH AND MENTAL HEALTH SERVICES RESEARCH, 2018, 45 (02) : 302 - 317
  • [35] Quantifying Patient Preferences for Treatment Outcomes in AML: A Discrete-Choice Experiment
    Richardson, Daniel R.
    Seo, Jaein
    Smith, Douglas
    Estey, Elihu H.
    O'Donoghue, Bernadette
    Bridges, John F. P.
    BLOOD, 2018, 132
  • [36] Patient preferences for topical treatment of actinic keratoses: a discrete-choice experiment
    Kopasker, D.
    Kwiatkowski, A.
    Matin, R. N.
    Harwood, C. A.
    Ismail, F.
    Lear, J. T.
    Thomson, J.
    Hasan, Z.
    Wali, G. N.
    Milligan, A.
    Crawford, L.
    Ahmed, I.
    Duffy, H.
    Proby, C. M.
    Allanson, P. F.
    BRITISH JOURNAL OF DERMATOLOGY, 2019, 180 (04) : 902 - 909
  • [37] Patient Preferences for Unresectable Hepatocellular Carcinoma Treatments: A Discrete-Choice Experiment
    Li, Daneng
    Tan, Ruoding
    Hernandez, Sairy
    Reilly, Norelle
    Bussberg, Cooper
    Mansfield, Carol
    CANCERS, 2023, 15 (05)
  • [38] Attributes for a discrete-choice experiment on preferences of patients for oncology pharmacy consultations
    Damerval, Margaux
    Bennani, Mohammed
    Rioufol, Catherine
    Omrani, Selim
    Riboulet, Margaux
    Etienne-Selloum, Nelly
    Saint-Ghislain, Audrey
    Leenhardt, Fanny
    Schmitt, Antonin
    Simon, Nicolas
    Clairet, Anne-Laure
    Meurisse, Aurelia
    Nerich, Virginie
    SUPPORTIVE CARE IN CANCER, 2024, 32 (05)
  • [39] Patient preferences for chronic lymphocytic leukemia treatments: a discrete-choice experiment
    Ravelo, Arliene
    Myers, Kelley
    Brumble, Robyn
    Bussberg, Cooper
    Koffman, Brian
    Manzoor, Beenish S.
    Biondo, Juliana M.
    Mansfield, Carol
    FUTURE ONCOLOGY, 2024, 20 (28) : 2059 - 2070
  • [40] A discrete-choice experiment to assess treatment modality preferences of patients with depression
    Lokkerbol, Joran
    Geomini, Amber
    van Voorthuijsen, Jule
    van Straten, Annemieke
    Tiemens, Bea
    Smit, Filip
    Risseeuw, Anneriek
    Hiligsmann, Mickael
    JOURNAL OF MEDICAL ECONOMICS, 2019, 22 (02) : 178 - 186