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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".