Evidence map›Paper›PMID 35906602›Full record

ArticleImplementation science : IS2022

Methods for capturing and analyzing adaptations: implications for implementation research.

Jodi Summers Holtrop, Dennis Gurfinkel, Andrea Nederveld, Phoutdavone Phimphasone-Brady, Patrick Hosokawa, Claude Rubinson, Jeanette A Waxmonsky, Bethany M Kwan

Registry-linked trialAbstract read
In one paragraph

Article in Implementation science : IS, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03590041 (Invested in Diabetes), which is not on this map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03590041 nacompletednot on this map

Invested in Diabetes: Comparing Patient-Centered Outcomes of Standardized Versus Patient-Driven Diabetes Shared Medical Appointments (PCORI IHS-1609-36322)

TypeinterventionalSponsorUniversity of Colorado, DenverRan2019 to 2022Enrolled1,085ConditionsDiabetes Mellitus, Type 2ArmsStandardized SMA, Patient-driven SMA
3 · Its place in the literature

Who cites it

34 citing papers in PubMed.

  1. Trial
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  4. Sustaining Diabetes Shared Medical Appointments After a Pragmatic Trial.Journal of the American Board of Family Medicine : JABFM
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  14. Modifications of a Parenting Program in the Context of Scaling-Up and Scaling-Out: Documenting Furaha Teens in Tanzania Using FRAME.Prevention science : the official journal of the Society for Prevention Research · 2025
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  16. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jodi Summers HoltropDepartment of Family Medicine, University of Colorado, Aurora, CO, 80045, USA. Jodi.Holtrop@CUAnschutz.edu.ORCID 0000-0002-5301-4014
Dennis GurfinkelAdult and Child Center for Outcomes Research and Delivery Science (ACCORDS), University of Colorado, Aurora, CO, USA.
Andrea NederveldDepartment of Family Medicine, University of Colorado, Aurora, CO, 80045, USA.
Phoutdavone Phimphasone-BradyDepartment of Psychiatry, University of Colorado, Aurora, CO, USA.
Patrick HosokawaAdult and Child Center for Outcomes Research and Delivery Science (ACCORDS), University of Colorado, Aurora, CO, USA.
Claude RubinsonUniversity of Houston-Downtown, Houston, TX, USA.
Jeanette A WaxmonskyDepartment of Family Medicine, University of Colorado, Aurora, CO, 80045, USA.
Bethany M KwanDepartment of Family Medicine, University of Colorado, Aurora, CO, 80045, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInterventions are often adapted; some adaptations may provoke more favorable outcomes, whereas some may not. A better understanding of the adaptations and their intended goals may elucidate which adaptations produce better outcomes. Improved methods are needed to better capture and characterize the impact of intervention adaptations.

methodsWe used multiple data collection and analytic methods to characterize adaptations made by practices participating in a hybrid effectiveness-implementation study of a complex, multicomponent diabetes intervention. Data collection methods to identify adaptations included interviews, observations, and facilitator sessions resulting in transcripts, templated notes, and field notes. Adaptations gleaned from these sources were reduced and combined; then, their components were cataloged according to the framework for reporting adaptations and modifications to evidence-based interventions (FRAME). Analytic methods to characterize adaptations included a co-occurrence table, statistically based k-means clustering, and a taxonomic analysis.

resultsWe found that (1) different data collection methods elicited more overall adaptations, (2) multiple data collection methods provided understanding of the components of and reasons for adaptation, and (3) analytic methods revealed ways that adaptation components cluster together in unique patterns producing adaptation "types." These types may be useful for understanding how the "who, what, how, and why" of adaptations may fit together and for analyzing with outcome data to determine if the adaptations produce more favorable outcomes rather than by adaptation components individually.

conclusionAdaptations were prevalent and discoverable through different methods. Enhancing methods to describe adaptations may better illuminate what works in providing improved intervention fit within context.

trial registrationThis trial is registered on clinicaltrials.gov under Trial number NCT03590041 , posted July 18, 2018.

Indexed as

Diabetes MellitusHumansAdaptationImplementation fidelityQualitative methodsShared medical appointments

Identifiers

PMID35906602
PMCPMC9335955

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.