Locating longitudinal study participants 10 years after last contact: Contemporary approaches to sample retention
Bibliographic record
Abstract
AIMS: Despite their advantages, longitudinal studies often face high rates of attrition. This study documents the extensive efforts associated with retaining a longitudinal cohort last contacted 10 years earlier. METHOD: We examine the processes and outcomes of attempts to reach 1736 individuals who have been part of a multiwave study about growing up in Ontario, Canada. Contact methods include email, phone, text, social media, postal mail, announcements in newspapers, subway stations, and music streaming services. RESULTS: Challenges included a lack of consistent annual communication with participants, children moving out of the parental home, and changes in email addresses and phone numbers. The most effective contact method was phone; text messages and friend referrals were the least effective. Overall, 41.5% of the original sample was reached. Locating former research participants years later necessitated multiple and repeated contact attempts, and intensive human resources. CONCLUSION: Ten lessons for effective sample retention are discussed. In summary, reducing attrition depends on a comprehensive study design and an organized and flexible protocol that adapts to a study's ever-changing needs.
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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 | 0.414 | 0.434 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.013 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".