EFFECTIVENESS OF RECRUITMENT STRATEGIES FOR A PHYSICAL ACTIVITY INTERVENTION IN OLDER ADULTS WITH CHRONIC DISEASES
Bibliographic record
Abstract
Engaging sedentary individuals in physical activity (PA) is a challenging endeavor. This poses a problem for PA interventions requiring large sample sizes from a representative population. Effective and feasible recruitment strategies for health-related studies involving PA interventions for community-dwelling older adults (OA) with chronic diseases has not been determined. For research projects to be carried out in reasonable time frames and with appropriate sample populations, information regarding optimum recruitment methods is essential. PURPOSE To compare the effectiveness of various recruitment strategies for enrollment into a PA intervention comparing class-based and community-based programs for OA with chronic disease. METHODS Two recruitment strategies were utilized to target healthcare professionals (HP) and the general public (GNP): (1) print media (posters, brochures, newspaper ads); (2) personal contact involving presentations to HP and organizations. Strategies targeting family physicians and other HP involved in-person contacts supplemented with print media. Recruitment of GNP was based on a self-referral system focusing on mass distribution of print media with less emphasis on personal contact. RESULTS Within weeks of initiation, the recruitment strategy generated 153 inquires. The most successful recruitment method was via advertising to GNP, accounting for 61% of the total inquiries. Physician referral yielded 23% of calls, however this reflects only a 10% response rate among physicians. Referrals from other primary HP resulted in 16% of inquiries. CONCLUSION Mass distribution of print material to GNP provided an effective means of generating interest in PA participation, although in-person contact served to enhance this recruitment strategy. Findings suggest various recruitment strategies should be employed, targeting the general population through self-referral. Supported by CIHR
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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 | 0.056 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".