A Thematic Analysis of the Impact of Community Engagement Studios on Community Experts’ Attitudes, Desires, and Understanding of Research
Bibliographic record
Abstract
Community Engagement Studios (CE Studios) are consultative sessions designed to provide community feedback on all stages of the research process from design to dissemination. CE Studios allow researchers to examine ways to enhance clinical and translational research by engaging with community members. Community members who are part of the patient population or target audience are defined as community experts. The purpose of this study is to examine community experts’ attitudes, desires, and understanding of research resulting from their participation in CE Studios. We conducted thematic analysis across three separate questions. Ten major themes emerged from the data: involvement, togetherness, trust, value, confidence, community engagement, community connectedness, encouraging others to participate, increasing knowledge and awareness, and respect. One overarching theme of inclusion was also presented in the data. Results indicate that CE Studios provide a space for community experts to gain a better understanding of the multifaceted research process, provide insight into ways to target historically excluded populations, and increase experts’ trust, confidence, and respect for researchers and the research process. Moreover, community experts felt connected to their community by participating in CE Studios and expressed interest in encouraging others to participate. Research studies should capitalize on CE Studios as a strategy to engage community members throughout the research process. Future research should determine whether CE Studios serve as a springboard to other leadership roles for community members and whether patient engagement models have a greater impact on patients and communities of color, rural environments, and other patient populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.072 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".