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Record W4411673875 · doi:10.3389/fpubh.2025.1569320

A 5-year examination of CAPABLE implementation using RE-AIM and CFIR frameworks

2025· article· en· W4411673875 on OpenAlexaboutno aff
Deborah Paone, Jeanne W. Schuller, Matthew Lee Smith, Laura N. Gitlin, Sarah L. Szanton

Bibliographic record

VenueFrontiers in Public Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
FundersNational Institute on Disability, Independent Living, and Rehabilitation ResearchHillman FoundationAustralian Government
KeywordsImplementation researchLicensureProcess managementProcess (computing)Medical educationComputer scienceMedicineNursingPsychological interventionBusiness

Abstract

fetched live from OpenAlex

Background: Examining the experience of organizations implementing evidence-based programs can help future programs address barriers to effective implementation, sustainment, and scaling. CAPABLE is an evidence-based 4-to-6-month program that improves daily function of older adults and modifies their home environments in modest ways to support their goal attainment. Through a guided process, utilizing an occupational therapist, nurse, and handy worker, the older adult sets goals and a personal action plan. In this study, we examined factors that advanced or impeded implementation and sustainability of CAPABLE. The researchers are embedded in the CAPABLE National Center and Johns Hopkins and provide ongoing technical support in implementation and dissemination of CAPABLE throughout the U.S., Canada, and other countries. Methods: . We identified key components to implement CAPABLE and used self-reported data from the lead program administrator at each organization who replied to the annual survey. These key informants responded to the level of ease or difficulty of these key components required for implementation. They responded each year that their organizations provided CAPABLE. CAPABLE licensure records indicated when the organization began/terminated their service. Notes from monthly office hours calls provided additional contextual information. We performed qualitative thematic and descriptive analysis on the notes. We also reviewed published studies on CAPABLE's outcomes. The unit of analysis was the organization. Results: The following factors were consistently reported by these administrators as supporting ease of implementation: getting leadership support, accessing technical assistance, and maintaining fidelity to the program. Conversely, common challenges reported included difficulty with recruitment, hiring/finding the required personnel, and sustainability funding. Internal factors supporting readiness and adoption were perceived value of the program and program manager knowledge and commitment. External factors reported that supported adoption was initial funding to start a pilot, and alignment with "aging in community" strategic goals. Implication: This examination revealed positive and impeding forces for implementation and sustainment and identified where additional support was needed. Findings are guiding the development of this additional technical support by the CAPABLE National Center. In addition, efforts are underway to improve funding and policy to support CAPABLE to improve sustainment, scaling, and dissemination. This study also provides a use case for employing the RE-AIM and CFIR frameworks together to track ongoing implementation. This helps address a gap in the literature concerning practical ways to monitor, evaluate, and report on ongoing implementation of evidence-based programs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.055
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.293

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.065
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0070.005
Science and technology studies0.0040.005
Scholarly communication0.0050.009
Open science0.0030.008
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.376
GPT teacher head0.622
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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