Effectiveness of Recruitment Strategies for a Physical Activity Intervention in Older Adults With Chronic Diseases
Bibliographic record
Abstract
Background: Engaging sedentary individuals in physical activity (PA) is challenging and problematic for research requiring large, representative samples. For research projects to be carried out in reasonable timeframes, optimum recruitment methods are needed. Effective recruitment strategies involving PA interventions for older adults have not been determined. Purpose: To compare the effectiveness of recruitment strategies for a PA intervention. Methods: Two recruitment strategies, print media and personal contact, targeted health-care professionals and the general public. Results: The strategies generated 581 inquiries; 163 were randomized into the study. Advertising to the general public via print materials and group presentations accounted for 78% of the total inquiries. Referrals from physicians and health-care professionals resulted in 22% of the inquiries. Conclusion: Mass distribution of print material to the general public, enhanced by in-person contact, was the most effective recruitment strategy. These findings suggest various recruitment strategies targeting the general population should be employed.
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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 | 0.033 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".