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Modeling the Information Preferences of Parents of Children with Mental Health Problems: A Discrete Choice Conjoint Experiment

Abstract

Although materials informing parents about children’s mental health (CMH) problems can improve outcomes, we know relatively little about the design factors that might influence their utilization of available resources. We used a discrete choice conjoint experiment to model the information preferences of parents seeking mental health services for 6 to 18 year olds. Parents completed 30 choice tasks presenting experimentally varied combinations of 20 four-level CMH information content, transfer process, and outcome attributes. Latent class analysis revealed three segments with different preferences. Parents in the Action segment (43%) chose materials providing step-by-step solutions to behavioral or emotional problems. They preferred weekly meetings with other parents and coaching calls from a therapist. The Information segment (41%) chose materials helping them understand rather than solve their child’s problems. These parents were more sensitive to logistical factors such as receiving information in groups, the location where information was available, the modality in which the information was presented, and the time required to obtain and use the information. The Overwhelmed segment (16%) reported more oppositional and conduct problems, felt their children’s difficulties exerted a greater adverse impact on family functioning, and reported higher personal depression scores than those in the Action or Information segments. Nonetheless, they did not choose information about, or solutions to, the problems their children presented. Simulations predicted that maximizing utilization and realizing the potential benefits of CMH information would require knowledge transfer strategies consistent with each segment’s preferences.

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Fig. 1

Notes

  1. 1.

    Briefly, CBC/HB uses Bayes theorem and simulated Monte Carlo Markov Chain processes (e.g. Gibbs Sampling) to estimate partworths or conjoint utilities for each participant. This program is referred to as hierarchical Bayes because the estimation process samples from two distributions of scores: (1) an upper level model which estimates part-worth utility averages and variances for the sample population, and (2) a lower level model drawing on the choices of each respondent in the study sample (Orme 2006).

  2. 2.

    Randomized First Choice Simulations improve prediction by estimating both attribute variability and product variability (Huber et al. 2007). We computed 100,000 sampling iterations to stabilize estimates.

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Correspondence to Charles E. Cunningham.

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Dr. Cunningham is Professor, Department of Psychiatry and Behavioural Neurosciences and the Jack Laidlaw Chair in Patient-Centred Health Care. Dr. Deal is Associate Professor of Strategic Market Leadership and Health Services Management, DeGroote School of Business. Heather Rimas is the Research Coordinator of the Patient-Centred Service Research Unit. Michelle Gold is now at the Canadian Mental Health Association, Ontario Division. This project was supported by a grant from the Canadian Institute of Health Research and the Jack Laidlaw Chair in Patient-Centred Health at McMaster University Faculty of Health Sciences. Dr. Boyle is Professor, Department of Psychiatry and Behavioral Neuroscience, Michelle Gold is now at the Canadian Mental Health Foundation, Toronto, Ontario. We acknowledge the assistance of Contact Hamilton, Peel Children’s Services, Kinark Child and Family Services, and the Child Parent Resource Institute.

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Cunningham, C.E., Deal, K., Rimas, H. et al. Modeling the Information Preferences of Parents of Children with Mental Health Problems: A Discrete Choice Conjoint Experiment. J Abnorm Child Psychol 36, 1123 (2008). https://doi.org/10.1007/s10802-008-9238-4

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Keywords

  • Conjoint analysis
  • Children’s mental health information
  • Parent preferences