Volunteer engagement to inform research on cardiovascular health awareness, Canada
Bibliographic record
Abstract
Volunteers have been extensively used in health promotion programmes. However, they have been less frequently involved in the research process. In its most recent iterations, the Cardiovascular Health Awareness Program (CHAP) integrated volunteers (i) to facilitate CHAP sessions with participating patients for data collection and (ii) to evaluate the intervention. Drawing on the patient and public involvement literature, our research team included volunteers in the data collection and evaluation of CHAP sessions as part of the programme's implementation in the province of Quebec (Canada). We sought volunteers' formal feedback through individual online and phone interviews and through focus groups for each of the four projects conducted in Quebec. We found that volunteers provide valuable insight on the research protocol as well as patient needs. Their feedback led to several modifications to the research protocol and procedures of subsequent CHAP sessions. Changes included involving volunteers at earlier stages of the research process, adding more learning modules and practice sessions during the volunteer training and defining research priorities according to patient needs. Our methodology of engaging volunteers in the research process was useful to gain important and unique insight on patient needs and for future programme planning to modify the research process.
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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 | 0.058 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".