ArticleImplementation science communications2023
Challenges and recommendations for measuring time devoted to implementation and intervention activities in health equity-focused, resource-constrained settings: a qualitative analysis.
Article in Implementation science communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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.
Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Investing in Infrastructure: A Mixed-Methods Cost Analysis of Implementation Strategies to Address Emerging Youth Drug Trends.Journal of studies on alcohol and drugs · 2026Article
- Practical considerations for engaging staff in resource-constrained healthcare settings in implementation research: A qualitative focus group and consensus building study.Journal of clinical and translational science · 2025Article
- Making Implementation Costing More Accessible: Initial Transdisciplinary Guidance for Researchers and Practitioners.Implementation research and practiceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundThere is little guidance for conducting health equity-focused economic evaluations of evidence-based practices in resource-constrained settings, particularly with respect to staff time use. Investigators must balance the need for low-touch, non-disruptive cost data collection with the need for data on providing services to priority subpopulations.
methodsThis investigation took place within a pilot study examining the implementation of a bundled screening intervention combining screening for social determinants of health and colorectal cancer at four federally qualified health centers (FQHCs) in the Boston metropolitan area. Methods for collecting data on personnel costs for implementation and intervention activities, including passive (automatic) and active (non-automatic, requiring staff time and effort) data collection, as well as three alternate wordings for self-reporting time-use, were evaluated qualitatively using data collected through interviews with FQHC staff (including clinicians, population health staff, and community health workers) and assessments of data completeness.
resultsPassive data collection methods were simple to execute and resulted in no missing data, but missed implementation and intervention activities that took place outside planned meetings. Active cost data collection using spreadsheets was simple for users when applied to care processes already tracked in this fashion and yielded accurate time use data. However, for tasks where this was not typical, and when tasks were broken up over multiple sessions, spreadsheets were more challenging to use. Questions asking about time use for a typical rather than specific time period, and for typical patients, yielded the most reliable and actionable data. Still, even the best-performing question had substantial variability in time use estimates. Participants noted that patient characteristics of interest for equity-focused research, including language spoken, adverse social determinants of health, and issues related to poverty or mental health, all contributed significantly to this variability.
conclusionsPassively collected time use data are the least burdensome and should be pursued in research efforts when possible, but should be accompanied by qualitative assessments to ensure the data are an accurate reflection of effort. When workflows are already tracked by active data collection, these are also strong data collection methods. Self-reported time use will be most accurate when questions inquire about "typical" tasks and specific types of patients.
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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.