Recruitment of multiple stakeholders to health services research: Lessons from the front lines
Bibliographic record
Abstract
BACKGROUND: Self-administered surveys are an essential methodological tool for health services and knowledge translation research, and engaging end-users of the research is critical. However, few documented accounts of the efforts invested in recruitment of multiple different stakeholders to one health services research study exist. Here, we highlight the challenges of recruiting key stakeholders (policy-makers, clinicians, guideline developers) to a Canadian Institutes of Health Research (CIHR) funded health services research (HSR) study aimed to develop an updated and refined version of a guideline appraisal tool, the AGREE. METHODS: Using evidence-based methods of recruitment, our goal was to recruit 192 individuals: 80 international guideline developers, 80 Canadian clinicians and 32 Canadian policy/decision-makers. We calculated the participation rate and the recruitment efficiency. RESULTS: We mailed 873 invitation letters. Of 838 approached, our participation rate was 29%(240) and recruitment efficiency, 19%(156). One policy-maker manager did not allow policy staff to participate in the study. CONCLUSIONS: Based on the results from this study, we suggest that future studies aiming to engage similar stakeholders in HSR over sample by at least 5 times to achieve their target sample size and allow for participant withdrawals. We need continued efforts to communicate the value of research between researchers and end-users of research (policy-makers, clinicians, and other researchers), integration of participatory research strategies, and promotion of the value of end-user involvement in research. Future research to understand methods of improving recruitment efficiency and engaging key stakeholders in HSR is warranted.
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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 | 0.624 | 0.594 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.028 | 0.029 |
| Scholarly communication | 0.031 | 0.046 |
| Open science | 0.018 | 0.045 |
| Research integrity | 0.025 | 0.032 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".