Enrollment cost and recruitment effectiveness in a deep transcranial magnetic stimulation clinical trial for older adults with depression: a mixed-methods analysis of recruitment strategies, facilitators, and barriers
Bibliographic record
Abstract
OBJECTIVE: Recruitment challenges are inherent in clinical research and are particularly pronounced in older adults with depression, who often face unique barriers such as medical comorbidities and infrequent help-seeking behavior. Neurostimulation techniques, like deep transcranial magnetic stimulation (dTMS), are often unfamiliar to patients. The literature offers limited insight into the costs and practical guidance associated with recruitment strategies in dTMS trials. This study aimed to address these gaps by investigating the cost-effectiveness of various recruitment strategies for dTMS trials among older adults, in addition to potential facilitators and barriers to recruitment. METHODS: This mixed-methods retrospective analysis examined recruitment data from our pilot study investigating the effects of dTMS in older adults with depression. We assessed diverse recruitment methods by analyzing enrollment rates and conducting an enrollment-cost analysis. Recruitmentrelated barriers and facilitators were identified through a theoretical thematic analysis. RESULTS: Over 14 months, we received 185 referrals, resulting in 22 enrolled participants. Health care provider outreach to affiliated mental health clinics was the most effective recruitment method, with an enrollment-cost rate of 0.00189 (CAD 537.63/person enrolled). The second most effective recruitment method was Facebook, yielding an enrollment-cost rate of 0.00099 (CAD 925.93/person enrolled). Social support from research personnel was a potential facilitator of recruitment, while timeintensiveness and accessibility challenges were noted as potential barriers. CONCLUSION: Our findings highlight both health care provider outreach within mental health clinics and Facebook advertising as effective recruitment strategies. Future research is needed to evaluate recruitment-related facilitators and barriers to dTMS interventions for older adults with depression.
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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 | 0.254 | 0.357 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".