ArticleJournal of clinical and translational science2023
Lay health worker research personnel for home-based data collection in clinical and translational research: Qualitative and quantitative findings from two trials in hard-to-reach populations.
Article in Journal of clinical and translational science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Aims: The role of lay health workers in data collection for clinical and translational research studies is not well described. We explored lay health workers as data collectors in clinical and translational research studies. We also present several methods for examining their work, i.e., qualitative interviews, fidelity checklists, and rates of unusable/missing data. Methods: We conducted 2 randomized, controlled trials that employed lay health research personnel (LHR) who were employed by community-based organizations. In one study, Results: Independent observers reported a very high degree of fidelity to technical data collection protocols (>95%) and low rates of missing/unusable data (1.5%-11%). Qualitative results show that trust, training, communication, and supervision are key and that LHR report feeling empowered by their role. LHR training included various content areas over several weeks with special attention to LHR and participant safety. Training and supervision from both the academic researchers and the staff at the community-based organizations were necessary and had to be well-coordinated. Conclusions: Carefully selected, trained, and supervised LHRs can collect sophisticated data for community-based clinical and translational research.
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Registered trials
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