Preferences for evidence‐based practice dissemination in addiction agencies serving women: a discrete‐choice conjoint experiment

Cunningham, Charles E.; Henderson, Joanna; Niccols, Alison; Dobbins, Maureen; Sword, Wendy; Chen, Yvonne; Mielko, Stephanie; Milligan, Karen; Lipman, Ellen L.; Thabane, Lehana

doi:10.1111/j.1360-0443.2012.03832.x

Cited by 18 publications

(18 citation statements)

References 84 publications

(163 reference statements)

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“…First, the process of eliciting preferences is, in and of itself, a way to increase stakeholder engagement and buy-in, a key component of the implementation process (6)(7)(8). Second, there is evidence that tailored implementation strategies (i.e., those that address localized barriers) are more effective than non-tailored strategies (9,10) and stakeholder preferences may provide insights regarding how to tailor to local contexts (9). Third, because stakeholder preferences may not align with evidence on what works, understanding their preferences is an essential rst step in determining where implementation efforts should start in terms of targeted mechanisms of change.…”

Section: Introductionmentioning

confidence: 99%

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

Williams

Candon

Stewart

et al. 2020

Preprint

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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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Section: Introductionmentioning

confidence: 99%

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

Williams

Candon

Stewart

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Cunningham and colleagues (this issue) explore factors weighed by clinicians when deciding whether to participate in knowledge translation (KT) activities . This information can be used to market KT or the dissemination of scientific findings to promote implementation.…”

Section: Notementioning

confidence: 99%

Commentary on Cunningham et al. (2012): Benefit to clients – outcome monitoring and knowledge translation

Gifford

2012

Addiction

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“…For DCE designs exploring a large number of variables, where presenting all combinations of alternatives is not feasible, a fractional factorial design can be used to determine participant preferences. For example, Cunningham et al 15 investigated the most preferred knowledge translation approaches among individuals working in addiction agencies for women. They investigated 16 different four-level knowledge dissemination variables in a preference survey of 18 choice tasks, three alternatives per choice task, and 999 blocks.…”

Section: Introductionmentioning

confidence: 99%

“…Blocks are surveys containing a different set of choice tasks (ie, presenting different combinations of alternatives), where individuals are randomly assigned to a block. 15 To create a full factorial design with 16 four-level attributes, a total of 4 294 967 296 (4 16 ) different hypothetical alternatives are needed. Cunningham et al created a design with 999 blocks of 18 choice tasks and three alternatives per choice task.…”

Section: Introductionmentioning

confidence: 99%

Simulation study to determine the impact of different design features on design efficiency in discrete choice experiments

et al. 2016

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ObjectivesDiscrete choice experiments (DCEs) are routinely used to elicit patient preferences to improve health outcomes and healthcare services. While many fractional factorial designs can be created, some are more statistically optimal than others. The objective of this simulation study was to investigate how varying the number of (1) attributes, (2) levels within attributes, (3) alternatives and (4) choice tasks per survey will improve or compromise the statistical efficiency of an experimental design.Design and methodsA total of 3204 DCE designs were created to assess how relative design efficiency (d-efficiency) is influenced by varying the number of choice tasks (2–20), alternatives (2–5), attributes (2–20) and attribute levels (2–5) of a design. Choice tasks were created by randomly allocating attribute and attribute level combinations into alternatives.OutcomeRelative d-efficiency was used to measure the optimality of each DCE design.ResultsDCE design complexity influenced statistical efficiency. Across all designs, relative d-efficiency decreased as the number of attributes and attribute levels increased. It increased for designs with more alternatives. Lastly, relative d-efficiency converges as the number of choice tasks increases, where convergence may not be at 100% statistical optimality.ConclusionsAchieving 100% d-efficiency is heavily dependent on the number of attributes, attribute levels, choice tasks and alternatives. Further exploration of overlaps and block sizes are needed. This study's results are widely applicable for researchers interested in creating optimal DCE designs to elicit individual preferences on health services, programmes, policies and products.

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Preferences for evidence‐based practice dissemination in addiction agencies serving women: a discrete‐choice conjoint experiment

Abstract: Clinicians and administrators are more likely to adopt evidence-based addiction practices if the practice is seen as helpful to clients, and if it is supported by co-workers and program administration.

Cited by 18 publications

References 84 publications

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

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

Commentary on Cunningham et al. (2012): Benefit to clients – outcome monitoring and knowledge translation

Simulation study to determine the impact of different design features on design efficiency in discrete choice experiments

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