Literature DB >> 19455413

Modeling the bullying prevention program preferences of educators: a discrete choice conjoint experiment.

Charles E Cunningham1, Tracy Vaillancourt, Heather Rimas, Ken Deal, Lesley Cunningham, Kathy Short, Yvonne Chen.   

Abstract

We used discrete choice conjoint analysis to model the bullying prevention program preferences of educators. Using themes from computerized decision support lab focus groups (n = 45 educators), we composed 20 three-level bullying prevention program design attributes. Each of 1,176 educators completed 25 choice tasks presenting experimentally varied combinations of the study's attribute levels. Latent class analysis yielded three segments with different preferences. Decision Sensitive educators (31%) preferred that individual schools select bullying prevention programs. In contrast, Support Sensitive educators (51%) preferred that local school boards chose bullying prevention programs. This segment preferred more logistical and social support at every stage of the adoption, training, implementation, and long term maintenance processes. Cost Sensitive educators (16%) showed a stronger preference for programs minimizing costs, training, and implementation time demands. They felt prevention programs were less effective and that the time and space in the curriculum for bullying prevention was less adequate. They were less likely to believe that bullying prevention was their responsibility and more likely to agree that prevention was the responsibility of parents. All segments preferred programs supported by the anecdotal reports of colleagues from other schools rather than those based on scientific evidence. To ensure that the bullying prevention options available reflect the complex combination of attributes influencing real world adoption decisions, program developers need to accommodate the differing views of the Decision, Support, and Cost Sensitive segments while maximizing the support of parents and students.

Mesh:

Year:  2009        PMID: 19455413     DOI: 10.1007/s10802-009-9324-2

Source DB:  PubMed          Journal:  J Abnorm Child Psychol        ISSN: 0091-0627


  25 in total

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3.  Current and future challenges in school-based prevention: the researcher perspective.

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4.  Identifying program preferences through conjoint analysis: illustrative results from a parent sample.

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Journal:  Am J Health Promot       Date:  1993 Nov-Dec

Review 5.  A systematic review of school-based interventions to prevent bullying.

Authors:  Rachel C Vreeman; Aaron E Carroll
Journal:  Arch Pediatr Adolesc Med       Date:  2007-01

Review 6.  Applying theory-driven approaches to understanding and modifying clinicians' behavior: what do we know?

Authors:  Matthew B Perkins; Peter S Jensen; James Jaccard; Peter Gollwitzer; Gabriele Oettingen; Elizabeth Pappadopulos; Kimberly E Hoagwood
Journal:  Psychiatr Serv       Date:  2007-03       Impact factor: 3.084

7.  School climate and implementation of a preventive intervention.

Authors:  Anne Gregory; David B Henry; Michael E Schoeny
Journal:  Am J Community Psychol       Date:  2007-12

8.  Heuristics made easy: an effort-reduction framework.

Authors:  Anuj K Shah; Daniel M Oppenheimer
Journal:  Psychol Bull       Date:  2008-03       Impact factor: 17.737

9.  Eliciting public preferences for healthcare: a systematic review of techniques.

Authors:  M Ryan; D A Scott; C Reeves; A Bate; E R van Teijlingen; E M Russell; M Napper; C M Robb
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10.  Modeling the information preferences of parents of children with mental health problems: a discrete choice conjoint experiment.

Authors:  Charles E Cunningham; Ken Deal; Heather Rimas; Don H Buchanan; Michelle Gold; Katherine Sdao-Jarvie; Michael Boyle
Journal:  J Abnorm Child Psychol       Date:  2008-05-15
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  11 in total

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2.  Addressing core challenges for the next generation of type 2 translation research and systems: the translation science to population impact (TSci Impact) framework.

Authors:  Richard Spoth; Louise A Rohrbach; Mark Greenberg; Philip Leaf; C Hendricks Brown; Abigail Fagan; Richard F Catalano; Mary Ann Pentz; Zili Sloboda; J David Hawkins
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3.  Using Latent Class Analysis to Model Preference Heterogeneity in Health: A Systematic Review.

Authors:  Mo Zhou; Winter Maxwell Thayer; John F P Bridges
Journal:  Pharmacoeconomics       Date:  2018-02       Impact factor: 4.981

4.  A discrete choice conjoint experiment to evaluate parent preferences for treatment of young, medication naive children with ADHD.

Authors:  Daniel A Waschbusch; Charles E Cunningham; William E Pelham; Heather L Rimas; Andrew R Greiner; Elizabeth M Gnagy; James Waxmonsky; Gregory A Fabiano; Jessica A Robb; Lisa Burrows-Maclean; Mindy Scime; Martin T Hoffman
Journal:  J Clin Child Adolesc Psychol       Date:  2011

5.  Segmenting patients and physicians using preferences from discrete choice experiments.

Authors:  Ken Deal
Journal:  Patient       Date:  2014       Impact factor: 3.883

6.  Providing information to parents of children with mental health problems: a discrete choice conjoint analysis of professional preferences.

Authors:  Charles E Cunningham; Ken Deal; Heather Rimas; Yvonne Chen; Don H Buchanan; Kathie Sdao-Jarvie
Journal:  J Abnorm Child Psychol       Date:  2009-11

7.  Understanding preferences for disease-modifying drugs in osteoarthritis.

Authors:  Liana Fraenkel; Lisa Suter; Charles E Cunningham; Gillian Hawker
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8.  Adaptive choice-based conjoint analysis: a new patient-centered approach to the assessment of health service preferences.

Authors:  Charles E Cunningham; Ken Deal; Yvonne Chen
Journal:  Patient       Date:  2010-12-01       Impact factor: 3.883

9.  Modeling the Mental Health Practice Change Preferences of Educators: A Discrete-Choice Conjoint Experiment.

Authors:  Charles E Cunningham; Melanie Barwick; Kathy Short; Yvonne Chen; Heather Rimas; Jenna Ratcliffe; Stephanie Mielko
Journal:  School Ment Health       Date:  2014

10.  Prioritising health service innovation investments using public preferences: a discrete choice experiment.

Authors:  Seda Erdem; Carl Thompson
Journal:  BMC Health Serv Res       Date:  2014-08-28       Impact factor: 2.655

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