Effectiveness and cost analysis of methods used to recruit older adult sedative users to a deprescribing randomized controlled trial during the COVID-19 pandemic
Bibliographic record
Abstract
Background: Recruitment to clinical trials is a challenge for researchers that became more pronounced because of COVID-19 public health protective measures, especially with respect to studies enrolling older adults. We completed an effectiveness and cost analysis of the recruitment methods used in The Your Answers When Needing Sleep in New Brunswick (YAWNS NB) study, a randomized controlled trial of a deprescribing intervention that recruited older adults with chronic use of sedatives during the pandemic. Methods: Study recruitment began during the COVID-19 pandemic. Strategies included random digit dialing (RDD), a targeted mail campaign and advertising through newspapers, online platforms (Google and Facebook), and television. Other awareness raising and recruitment strategies involved seniors' organizations, pharmacies, television news stories, and referrals. Recruitment effectiveness and cost analysis involved enrollment rate (ER), cost per randomized participant (CPRP), fractional cost (FC), fractional enrollment (FE), fractional enrollment-cost ratio (FEC), and efficacy index (EI) calculations. Results: There were 1295 interested older adults with 594 randomized into the study for an enrollment rate of 46%. The efficacy index (EI) was highest for Facebook ads (EI = 0.683) followed by television (EI = 0.426), and newsprint ads (EI = 0.298). The cost of RDD was highest per randomized participant at $1117.90 and produced the lowest EI (0.013). Conclusion: Facebook ads had the best efficacy index for recruiting older adults to the YAWNS NB study during the COVID-19 pandemic and television ads produced the most enrollments. RDD was expensive and yielded few recruits. Recruitment costs can be significant for recruiting community-dwelling older adults. This experience can inform recruitment strategy and budget development for future community studies enrolling older adults, especially in the context of the COVID-19 pandemic.
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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 | 0.097 | 0.185 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.015 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".