Community stakeholder preferences for evidence-based practice implementation strategies in behavioral health: a best-worst scaling choice experiment

Abstract Background Community behavioral health clinicians, supervisors, and administrators play an essential role in implementing new psychosocial evidence-based practices (EBP) for patients receiving psychiatric care; however, little is known about these stakeholders’ values and preferences for implementation strategies that support EBP use, nor how best to elicit, quantify, or segment their preferences. This study sought to quantify these stakeholders’ preferences for implementation strategies and to identify segments of stakeholders with distinct preferences using a rigorous choice experiment method called best-worst scaling. Methods A total of 240 clinicians, 74 clinical supervisors, and 29 administrators employed within clinics delivering publicly-funded behavioral health services in a large metropolitan behavioral health system participated in a best-worst scaling choice experiment. Participants evaluated 14 implementation strategies developed through extensive elicitation and pilot work within the target system. Preference weights were generated for each strategy using hierarchical Bayesian estimation. Latent class analysis identified segments of stakeholders with unique preference profiles. Results On average, stakeholders preferred two strategies significantly more than all others—compensation for use of EBP per session and compensation for preparation time to use the EBP (P < .05); two strategies were preferred significantly less than all others—performance feedback via email and performance feedback via leaderboard (P < .05). However, latent class analysis identified four distinct segments of stakeholders with unique preferences: Segment 1 (n = 121, 35%) strongly preferred financial incentives over all other approaches and included more administrators; Segment 2 (n = 80, 23%) preferred technology-based strategies and was younger, on average; Segment 3 (n = 52, 15%) preferred an improved waiting room to enhance client readiness, strongly disliked any type of clinical consultation, and had the lowest participation in local EBP training initiatives; Segment 4 (n = 90, 26%) strongly preferred clinical consultation strategies and included more clinicians in substance use clinics. Conclusions The presence of four heterogeneous subpopulations within this large group of clinicians, supervisors, and administrators suggests optimal implementation may be achieved through targeted strategies derived via elicitation of stakeholder preferences. Best-worst scaling is a feasible and rigorous method for eliciting stakeholders’ implementation preferences and identifying subpopulations with unique preferences in behavioral health settings.

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Community stakeholder preferences for evidence-based practice implementation strategies in behavioral health: A best-worst scaling choice experiment

10.21203/rs.3.rs-55991/v2 ◽

2020 ◽

Author(s):

Nathaniel Williams ◽

Molly Candon ◽

Rebecca Stewart ◽

Y. Vivian Byeon ◽

Meenakshi Bewtra ◽

...

Keyword(s):

Latent Class Analysis ◽

Behavioral Health ◽

Performance Feedback ◽

Choice Experiment ◽

Latent Class ◽

Implementation Strategies ◽

Evidence Based ◽

Clinical Consultation ◽

Best Worst Scaling ◽

Stakeholder Preferences

Abstract Background: Community behavioral health clinicians, supervisors, and administrators play an essential role in implementing new psychosocial evidence-based practices (EBP) for patients receiving psychiatric care; however, little is known about these stakeholders’ values and preferences for implementation strategies that support EBP use, nor how best to elicit, quantify, or segment their preferences. This study sought to quantify these stakeholders’ preferences for implementation strategies and to identify segments of stakeholders with distinct preferences using a rigorous choice experiment method called best-worst scaling. Methods: A total of 240 clinicians, 74 clinical supervisors, and 29 administrators employed within clinics delivering publicly-funded behavioral health services in a large metropolitan behavioral health system participated in a best-worst scaling choice experiment. Participants evaluated 14 implementation strategies developed through extensive elicitation and pilot work within the target system. Preference weights were generated for each strategy using hierarchical Bayesian estimation. Latent class analysis identified segments of stakeholders with unique preference profiles. Results: On average, stakeholders preferred two strategies significantly more than all others—compensation for use of EBP per session and compensation for preparation time to use the EBP (P<.05); two strategies were preferred significantly less than all others—performance feedback via email and performance feedback via leaderboard (P<.05). However, latent class analysis identified four distinct segments of stakeholders with unique preferences: Segment 1 (n = 121, 35%) strongly preferred financial incentives over all other approaches and included more administrators; Segment 2 (n = 80, 23%) preferred technology-based strategies and was younger, on average; Segment 3 (n = 52, 15%) preferred an improved waiting room to enhance client readiness, strongly disliked any type of clinical consultation, and had the lowest participation in local EBP training initiatives; Segment 4 (n = 90, 26%) strongly preferred clinical consultation strategies and included more clinicians in substance use clinics. Conclusions: The presence of four heterogeneous subpopulations within this large group of clinicians, supervisors, and administrators suggests optimal implementation may be achieved through targeted strategies derived via elicitation of stakeholder preferences. Best-worst scaling is a feasible and rigorous method for eliciting stakeholders’ implementation preferences and identifying subpopulations with unique preferences in behavioral health settings.

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Community Stakeholder Preferences for Evidence-Based Practice Implementation Strategies in Behavioral Health: A Best-Worst Scaling Choice Experiment

10.21203/rs.3.rs-55991/v1 ◽

2020 ◽

Author(s):

Nathaniel Williams ◽

Molly Candon ◽

Rebecca Stewart ◽

Y. Vivian Byeon ◽

Meenakshi Bewtra ◽

...

Keyword(s):

Behavioral Health ◽

Performance Feedback ◽

Financial Incentives ◽

Choice Experiment ◽

Latent Class ◽

Implementation Strategies ◽

Evidence Based ◽

Clinical Consultation ◽

Best Worst Scaling ◽

Stakeholder Preferences

Abstract Background: Community behavioral health clinicians, supervisors, and administrators play an essential role in implementing new psychosocial evidence-based practices (EBP) for patients receiving psychiatric care; however, little is known about these stakeholders’ values and preferences for implementation strategies that support EBP use, nor how best to elicit, quantify, or segment their preferences. This study sought to quantify clinician, supervisor, and administrator preferences for implementation strategies and to identify segments of these stakeholders with distinct preferences using a rigorous choice experiment method called best-worst scaling (BWS). Methods: A total of 240 clinicians, 74 clinical supervisors, and 29 administrators delivering publicly funded behavioral health services in a large metropolitan behavioral health system participated in a best-worst scaling choice experiment. Participants evaluated 14 implementation strategies developed through extensive elicitation and pilot work within the target system. Preference weights were generated for each strategy using hierarchical Bayesian estimation. Latent class analysis identified segments of stakeholders with unique preference profiles. Results: On average, clinicians, supervisors, and administrators preferred two strategies significantly more than all others—compensation for use of EBP per session and compensation for preparation time to use the EBP; two strategies were preferred significantly less than all others—performance feedback via email and performance feedback via leaderboard. However, latent class analysis identified four distinct segments of stakeholders with unique preferences: Segment 1 (n = 121, 35%) strongly preferred financial incentives over all other approaches; Segment 2 (n = 80, 23%) preferred technology-based strategies; Segment 3 (n = 52, 15%) preferred an improved waiting room to enhance client readiness and strongly disliked any type of clinical consultation; Segment 4 (n = 90, 26%) rejected financial incentives and strongly preferred strategies focused on clinical consultation. Conclusions: The presence of four heterogeneous subpopulations within this large group of behavioral health administrators, supervisors, and clinicians suggests optimal implementation may be achieved through targeted strategies derived via elicitation of stakeholder preferences. Best-worst scaling is a feasible, systematic, and rigorous method for eliciting stakeholders’ implementation preferences and identifying subpopulations with unique preferences in behavioral health settings.

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The Role of Trust in Explaining Food Choice: Combining Choice Experiment and Attribute Best–Worst Scaling

Foods ◽

10.3390/foods9010045 ◽

2020 ◽

Vol 9 (1) ◽

pp. 45

Author(s):

Ching-Hua Yeh ◽

Monika Hartmann ◽

Nina Langen

Keyword(s):

Latent Class Analysis ◽

Food Choice ◽

Logit Model ◽

Choice Experiment ◽

Latent Class ◽

Class Analysis ◽

Class Membership ◽

Credence Attributes ◽

Best Worst Scaling

This paper presents empirical findings from a combination of two elicitation techniques—discrete choice experiment (DCE) and best–worst scaling (BWS)—to provide information about the role of consumers’ trust in food choice decisions in the case of credence attributes. The analysis was based on a sample of 459 Taiwanese consumers and focuses on red sweet peppers. DCE data were examined using latent class analysis to investigate the importance and the utility different consumer segments attach to the production method, country of origin, and chemical residue testing. The relevance of attitudinal and trust-based items was identified by BWS using a hierarchical Bayesian mixed logit model and was aggregated to five latent components by means of principal component analysis. Applying a multinomial logit model, participants’ latent class membership (obtained from DCE data) was regressed on the identified attitudinal and trust components, as well as demographic information. Results of the DCE latent class analysis for the product attributes show that four segments may be distinguished. Linking the DCE with the attitudinal dimensions reveals that consumers’ attitude and trust significantly explain class membership and therefore, consumers’ preferences for different credence attributes. Based on our results, we derive recommendations for industry and policy.

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Understanding preferences of German flower consumers: the desire for sustained beauty

International Journal of Retail & Distribution Management ◽

10.1108/ijrdm-10-2017-0229 ◽

2018 ◽

Vol 46 (6) ◽

pp. 560-576

Author(s):

Meike Rombach ◽

Nicole Widmar ◽

Elizabeth Byrd ◽

Vera Bitsch

Keyword(s):

Latent Class Analysis ◽

Latent Class ◽

Consumer Preferences ◽

Federal State ◽

Class Analysis ◽

Cut Flower ◽

Cut Flowers ◽

Content Type ◽

Relative Preference ◽

Best Worst Scaling

PurposeThe purpose of this paper is to provide insights for flower retailers, horticultural practitioners and marketing managers into the prioritisation of cut flower attributes by German residents.Design/methodology/approachApplying a best–worst scaling approach, this analysis identified the relative ranking of importance amongst product attributes relevant to German consumers when buying fresh cut flowers. A latent class analysis determined four flower consumer segments for further study. The study builds on a sample of 978 consumers and is consistent with the most recent German census in terms of age, gender, income and federal state.FindingsThe best-worst analysis showed that intrinsic flower attributes, in particular appearance, freshness and scent were found to be more important to German consumers than the extrinsic attributes studied, namely, price, country of origin and a certification indicating fair trade. The latent class analysis determined four consumer segments that desire either budget, luxury or ethical flowers or more information about flowers. For all identified consumer segments, appearance was the attribute of greatest importance. The segments that desired luxury or ethical flowers, as well as the segment that desires more information were interested in appearance, but also had relatively large shares of preferences dedicated to flower freshness guarantees. The preference for freshness guarantees in addition to appearance may be interpreted jointly as a desire for not only beautiful and aesthetically pleasing flowers, but for sustained beauty.Originality/valueInternationally, the study fills a research gap by exploring consumer’s relative preference for cut flower attributes. In contrast to existing studies on consumer preferences for flowers in Germany, the present study builds on a sample that was targeted in terms of age, gender, net household income and federal state to the most recent German census.

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Hawaiian Residents’ Preferences for Miconia Control Program Attributes Using Conjoint Choice Experiment and Latent Class Analysis

Environmental Management ◽

10.1007/s00267-009-9415-4 ◽

2009 ◽

Vol 45 (2) ◽

pp. 250-260 ◽

Cited By ~ 9

Author(s):

Catherine Chan-Halbrendt ◽

Tun Lin ◽

Fang Yang ◽

Gwendalyn Sisior

Keyword(s):

Latent Class Analysis ◽

Choice Experiment ◽

Latent Class ◽

Control Program ◽

Class Analysis ◽

Conjoint Choice ◽

Conjoint Choice Experiment

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Incentivising doctor attendance in rural Bangladesh: a latent class analysis of a discrete choice experiment

BMJ Global Health ◽

10.1136/bmjgh-2021-006001 ◽

2021 ◽

Vol 6 (7) ◽

pp. e006001

Author(s):

Blake Angell ◽

Mushtaq Khan ◽

Raihanul Islam ◽

Kate Mandeville ◽

Nahitun Naher ◽

...

Keyword(s):

Latent Class Analysis ◽

Discrete Choice Experiment ◽

Discrete Choice ◽

Choice Experiment ◽

Latent Class ◽

Latent Class Models ◽

Class Analysis ◽

Sustainable Solutions ◽

Rural Bangladesh ◽

Incentive Payments

ObjectiveDoctor absenteeism is widespread in Bangladesh, and the perspectives of the actors involved are insufficiently understood. This paper sought to elicit preferences of doctors over aspects of jobs in rural areas in Bangladesh that can help to inform the development of packages of policy interventions that may persuade them to stay at their posts.MethodsWe conducted a discrete choice experiment with 308 doctors across four hospitals in Dhaka, Bangladesh. Four attributes of rural postings were included based on a literature review, qualitative research and a consensus-building workshop with policymakers and key health-system stakeholders: relationship with the community, security measures, attendance-based policies and incentive payments. Respondents’ choices were analysed with mixed multinomial logistic and latent class models and were used to simulate the likely uptake of jobs under different policy packages.ResultsAll attributes significantly impacted doctor choices (p<0.01). Doctors strongly preferred jobs at rural facilities where there was a supportive relationship with the community (β=0.93), considered good attendance in education and training (0.77) or promotion decisions (0.67), with functional security (0.67) and higher incentive payments (0.5 per 10% increase of base salary). Jobs with disciplinary action for poor attendance were disliked by respondents (−0.63). Latent class analysis identified three groups of doctors who differed in their uptake of jobs. Scenario modelling identified intervention packages that differentially impacted doctor behaviour and combinations that could feasibly improve doctors’ attendance.ConclusionBangladeshi doctors have strong but varied preferences over interventions to overcome absenteeism. We generated evidence suggesting that interventions considering the perspective of the doctors themselves could result in substantial reductions in absenteeism. Designing policy packages that take account of the different situations facing doctors could begin to improve their ability and motivation to be present at their job and generate sustainable solutions to absenteeism in rural Bangladesh.

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