A multi-method evaluation of how equity deserving communities were engaged in research
Bibliographic record
Abstract
BACKGROUND: We aimed to understand and evaluate the types, experiences and levels of engagement of all groups engaged in the OPTimise research study. The OPTimise study used virtual community engagement to engage equity-deserving populations across three sites to help public health units tailor strategies to improve vaccine uptake during COVID-19. Our evaluation was uniquely conducted at arm's length from the OPTimise researchers who facilitated the virtual engagement with these groups. METHOD: The community members involved in research (community leaders and citizen partners) were invited to complete the 22-item Patient Engagement in Research Scale (PEIRS-22) survey to understand the nature of engagement. Twenty-three participants were invited to complete the survey and 19 (83%) completed the survey. We conducted individual and focus group interviews with members of all four groups (community leaders, citizen partners, public health unit knowledge user and connectors, and methods researchers) to learn about their engagement experiences with the OPTimise study and research team. Thirty-five participants were invited to join an interview or focus group interview with 22 (63%) completing an interview. RESULTS: OPTimise study members reported "extremely meaningful" engagement which is the highest level on the PEIRS-22 scale. The interviews demonstrated that the OPTimise study was designed appropriately across the three sites. Interviews were analyzed based on four topics: relationship management, supporting processes, power sharing, and satisfaction with operations. Qualitative and quantitative data are compared to the Levels of Patient and Researcher Engagement in Health Research framework. Citizen partners and methods researchers reported being engaged at the level of "lead" on the framework. Community leaders reported their level of engagement as at the level of "involve" throughout the study while connectors and public health knowledge users reported their level of engagement was at the level of "involve episodically" throughout the study. CONCLUSIONS: This evaluation found that building authentic relationships between the OPTimise research team and participants was key in the early phases of the OPTimise study. Researchers on future projects could benefit from developing working relationships with organizations who can connect with the target audience to facilitate trust building between researchers and engagement participants, especially in virtual environments.
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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 | 0.128 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".