CAN‐THUMBS UP: Recruitment in the Virtual Era
Bibliographic record
Abstract
Abstract Background The Brain Health Support Program (BHSP) of the CAN‐Thumbs Up Study is an online, interactive educational intervention designed to increase dementia literacy, self‐efficacy and address modifiable lifestyle risk factors. Recruiting for a virtual study presented both opportunities and challenges. This included the need to attract participants through means beyond referrals from medical clinics, as well as opportunities to reach geographically diverse Canadian population less feasible with in‐person evaluations. Methods A comprehensive recruitment strategy for a virtual environment was implemented. A central website provided any interested candidate with study information, and a link to register and provide basic demographic information via a secure form. Potential participants were directed to the website via traditional recruitment through CCNA specialty clinics or existing cohorts, through geotargeting mailing to specific target populations throughout Canada, social medial advertising, partner organizations, and a coordinated National press release. Results The study had an 8‐month open recruitment window. During this time 964 participants agreed to be contacted through our online portal. Of these, 18‐81 were consented to enroll in the study per month. A total of 7,569 geotargeted postcards were sent, with 153 individuals agreeing to be contacted for the study (2%). Social media advertising generated 974 clicks, with 6.8% subsequently agreeing to be contacted for more information. The majority of participants enrolled in the study came from existing clinical cohorts, CCNA clinical sites, and partner organizations. The recruitment cohort was from a diverse geographical location, 45% urban, 35% suburban, and 20% rural. Overall, the target cohort of 350 subjects was successfully enrolled by the end of the recruitment period. Conclusion Recruitment for the BHSP intervention demonstrates the value of relatively underutilized clinical trials recruitment strategies, such as geotargeted mailing, and social media advertising. We also show the value of a centralized web site for initial screening of interested participants. While traditional recruitment methods still directed the majority of participants to the recruitment website, geotargeted mailing, social advertising, partner organizations, and earned media can all be scaled up, and will likely play a major role in future recruitments efforts in the broader CAN‐Thumbs Up Study.
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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 | 0.048 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.020 |
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".