Redesigning implementation measurement for monitoring and quality improvement in community delivery settings
Open accessAbstract
The field of prevention has established the potential to promote child adjustment across a wide array of outcomes. However, when evidence-based prevention programs have been delivered at scale in community settings, declines in implementation and outcomes have resulted. Maintaining high quality implementation is a critical challenge for the field. We describe steps towards the development of a practical system to monitor and support the high-quality implementation of evidence-based prevention programs in community settings. Research on the implementation of an evidence-based parenting program for divorcing families called the "New Beginnings Program" serves as an illustration of the promise of such a system. As a first step, we describe a multidimensional theoretical model of implementation that links aspects of program delivery with improvements in participant outcomes. We then describe research on the measurement of each of these implementation dimensions and test their relations to intended program outcomes. As a third step, we develop approaches to the assessment of these implementation constructs that are feasible to use in community settings and to establish their reliability and validity. We focus on the application of machine learning algorithms and web-based data collection systems to assess implementation and provide support for high quality delivery and positive outcomes. Examples are presented to demonstrate that valid and reliable measures can be collected using these methods. Finally, we envision how these measures can be used to develop an unobtrusive system to monitor implementation and provide feedback and support in real time to maintain high quality implementation and program outcomes.
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Such methods can reduce measurement burden, recall and reporting biases, and missing data.
“in addition to increasing scalability, they can reduce measurement burden, recall and reporting biases, and missing data (Berkel et al., 2019).”
From Measuring the active elements of cognitive-behavioral therapies (2023) · cites this paper as Berkel et al., 2019
In the Introduction’s “Towards Feasible Assessment of Participant Responsiveness” section (page unavailable), the paper says online data entry could reduce facilitator burden and that technology could help achieve complete data collection. It does not say these methods reduce recall or reporting biases; its discussion of transfer errors is a different point. (LLM, conf=0.85)
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Self-citation(2009)
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Research works printed but not linked: 2
- Gallo CG, Berkel C, Sandler IN, & Brown CH (2015). Improving implementation of behavioral interventions by monitoring quality of delivery in speech Paper presented at the annual Conference on the Science of Dissemination & Implementation, Washington, DC.
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Cited by
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Self-citationMeasuring the active elements of cognitive-behavioral therapies
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- Authors
- Berkel, C., Gallo, C. G., Sandler, I. N., Mauricio, A. M., Smith, J. D., & Brown, C. H.
- Affiliations
- Arizona State University (US)
- Northwestern University (US)
- Corresponding author
- Berkel, Cady
- ORCID
- Venue
- The Journal of Primary Prevention
- Published
- 2019-01-18
- Volume
- 40
- Issue
- 1
- Pages
- 111-127
- ISSN
- 0278-095X (ISSN-L)
- 1573-6547
- PMC
- PMC6404547
- OpenAlex
- W2910727329
- Cited by
- 27
- References
- 40
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 9 · 2022: 2 · 2021: 6 · 2020: 5 · 2019: 2
- FWCI
- 5.11
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- The Journal of Primary Prevention (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Drug Abuse (R01DA033991, R01DA026874, P30-DA027828, R01DA033991-03S1)
- Topics
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Social Sciences › Decision Sciences › Management Science and Operations Research › Evaluation and Performance Assessment
- Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
- Keywords
- implementation measurement, implementation monitoring, quality improvement, evidence-based prevention programs, parent training, divorce, child adjustment, implementation fidelity, machine learning, online data collection, real-time feedback, reliability and validity
- MeSH terms
- Machine Learning, Implementation Science, Adolescent, Adult, Child, Curriculum, Data Collection, Divorce, Female, Humans, Male, Models, Theoretical, Parent-Child Relations, Program Evaluation, Evidence-Based Practice, Quality Improvement
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