Evidence map›Paper›PMID 38249838›Full record

ArticleLearning health systems2024

Analysis of

Heather Z Mui, Cati G Brown-Johnson, Erika A Saliba-Gustafsson, Anna Sophia Lessios, Mae Verano, Rachel Siden, Laura M Holdsworth

Open access · goldAbstract read
In one paragraph

Article in Learning health systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.1field-weighted citation impact, top 9% of its field
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 7 citations in OpenAlex.

  1. Trial
  2. Article
  3. Review
  4. Article
  5. Article
  6. Analysis ofLearning health systems · 2024
    Article
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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

7 authors at 1 institution in 1 country.

Heather Z MuiDivision of Primary Care and Population Health, Department of Medicine School of Medicine, Stanford University Palo Alto California USA.ORCID https://orcid.org/0000-0002-2499-3583
Cati G Brown-JohnsonDivision of Primary Care and Population Health, Department of Medicine School of Medicine, Stanford University Palo Alto California USA.ORCID https://orcid.org/0000-0002-5415-3665
Erika A Saliba-GustafssonDivision of Primary Care and Population Health, Department of Medicine School of Medicine, Stanford University Palo Alto California USA.ORCID https://orcid.org/0000-0003-1587-9591
Anna Sophia LessiosDivision of Primary Care and Population Health, Department of Medicine School of Medicine, Stanford University Palo Alto California USA.ORCID https://orcid.org/0000-0003-3493-0540
Mae VeranoDivision of Primary Care and Population Health, Department of Medicine School of Medicine, Stanford University Palo Alto California USA.
Rachel SidenDivision of Primary Care and Population Health, Department of Medicine School of Medicine, Stanford University Palo Alto California USA.
Laura M HoldsworthDivision of Primary Care and Population Health, Department of Medicine School of Medicine, Stanford University Palo Alto California USA.ORCID https://orcid.org/0000-0003-0611-2700
VA Palo Alto Health Care System · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Tracking adaptations during implementation can help assess and interpret outcomes. The framework for reporting adaptations and modifications-expanded (FRAME) provides a structured approach to characterize adaptations. We applied the FRAME across multiple health services projects, and developed an analytic approach to assess the impact of adaptations. Methods: Mixed methods analysis of research diaries from seven quality improvement (QI) and research projects during the early stages of the COVID-19 pandemic. Using the FRAME as a codebook, discrete adaptations were described and categorized. We then conducted a three-step analysis plan: (1) calculated the frequency of adaptations by FRAME categories across projects; (2) qualitatively assessed the impact of adaptations on project goals; and (3) qualitatively assessed relationships between adaptations within projects to thematically consolidate adaptations to generate more explanatory value on how adaptations influenced intervention progress and outcomes. Results: Between March and July 2020, 42 adaptations were identified across seven health services projects. The majority of adaptations related to training or evaluation (52.4%) with the goal of maintaining the feasibility (66.7%) of executing projects during the pandemic. Five FRAME constructs offered the most explanatory benefit to assess the impact of adaptations on program and evaluation goals, providing the basis for creating an analytic approach dubbed the "A-FRAME," analysis of FRAME data. Using the A-FRAME, the 42 adaptations were consolidated into 17 succinct adaptations. Two QI projects discontinued altogether. Intervention adaptations related to staffing, training, or delivery, while evaluation adaptations included design, recruitment, and data collection adjustments. Conclusions: By sifting qualitative data about adaptations into the A-FRAME, implementers and researchers can succinctly describe how adaptations affect interventions and their evaluations. The simple and concise presentation of information using the A-FRAME matrix can help implementers and evaluators account for the influence of adaptations on program outcomes.

Indexed as

adaptationCOVID‐19 pandemichealth services researchimplementation sciencequality improvement

Identifiers

PMID38249838
PMCPMC10797575
OpenAlexW4327610645

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.