Effective Recruitment and Retention in a Longitudinal Panel Natural Experiment: The Study of Active Neighborhoods in Detroit (StAND)
Bibliographic record
Abstract
Objectives: Due to the limited evidence on recruitment and retention for longitudinal panel and natural experiments, we utilize findings from a natural experiment to identify barriers encountered, effectiveness of strategies employed, and recommendations. Methods: We evaluated recruitment strategies (doorto‐door contact, postcards, park events, mailed materials, provision of health results to participants, study branding, and participant compensation) and retention efforts (home visits, provision of contact information, birthday and holiday cards, newsletters, phone calls, and engaging with community supporters). We also recorded staff’s reported barriers each year. Results: Recruitment barriers included extreme weather, participant non-response, and high neighborhood vacancy. Door‐to‐door canvassing was the most and park events were the least successful methods for recruiting participants with multiple waves of data. Overall, 18% of participants were lost‐to‐follow‐up, which was highest for those recruited via park events from control neighborhoods (6.9%). For intervention neighborhoods, recruits from park events and from door‐to‐door canvassing had similar lost‐to‐follow‐up (2.9%). Withdrawal rates were low (2%); our retention strategies likely aided in this. Conclusions: We recommend: 1) varied in-person recruitment methods; 2) repeated participant contact through various means (e.g., text, mail); 3) study branding, 4) demographic diversity of staff; and 5) offering a range of participation levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".