Transforming Mental Health Delivery Through Behavioral Economics and Implementation Science: Protocol for Three Exploratory Projects
Bibliographic record
Abstract
BACKGROUND: Efficacious psychiatric treatments are not consistently deployed in community practice, and clinical outcomes are attenuated compared with those achieved in clinical trials. A major focus for mental health services research is to develop effective and cost-effective strategies that increase the use of evidence-based assessment, prevention, and treatment approaches in community settings. OBJECTIVE: The goal of this program of research is to apply insights from behavioral economics and participatory design to advance the science and practice of implementing evidence-based practice (EBP) for individuals with psychiatric disorders across the life span. METHODS: Project 1 (Assisting Depressed Adults in Primary care Treatment [ADAPT]) is patient-focused and leverages decision-making heuristics to compare ways to incentivize adherence to antidepressant medications in the first 6 weeks of treatment among adults newly diagnosed with depression. Project 2 (App for Strengthening Services In Specialized Therapeutic Support [ASSISTS]) is provider-focused and utilizes normative pressure and social status to increase data collection among community mental health workers treating children with autism. Project 3 (Motivating Outpatient Therapists to Implement: Valuing a Team Effort [MOTIVATE]) explores how participatory design can be used to design organizational-level implementation strategies to increase clinician use of EBPs. The projects are supported by a Methods Core that provides expertise in implementation science, behavioral economics, participatory design, measurement, and associated statistical approaches. RESULTS: Enrollment for project ADAPT started in 2018; results are expected in 2020. Enrollment for project ASSISTS will begin in 2019; results are expected in 2021. Enrollment for project MOTIVATE started in 2018; results are expected in 2019. Data collection had begun for ADAPT and MOTIVATE when this protocol was submitted. CONCLUSIONS: This research will advance the science of implementation through efforts to improve implementation strategy design, measurement, and statistical methods. First, we will test and refine approaches to collaboratively design implementation strategies with stakeholders (eg, discrete choice experiments and innovation tournaments). Second, we will refine the measurement of mechanisms related to heuristics used in decision making. Third, we will develop new ways to test mechanisms in multilevel implementation trials. This trifecta, coupled with findings from our 3 exploratory projects, will lead to improvements in our knowledge of what causes successful implementation, what variables moderate and mediate the effects of those causal factors, and how best to leverage this knowledge to increase the quality of care for people with psychiatric disorders. TRIAL REGISTRATION: ClinicalTrials.gov NCT03441399; https://www.clinicaltrials.gov/ct2/show/NCT03441399 (Archived by WebCite at http://www.webcitation.org/74dRbonBD). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12121.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".