Strategies for Increasing Youth Participation in Longitudinal Survey Research: Lessons from a Pilot Study
Bibliographic record
Abstract
The pilot phase of a research study is essential for refining methodological and theoretical aspects before a full-scale launch. Using participatory action research with youth and sector partners, this study tested the design and implementation of a longitudinal research project, focusing on four key areas: recruitment strategies, survey design, incentive strategies, and participant engagement and retention. The study compares two recruitment messages, assessed survey clarity and completion rates, tested financial and non-financial incentives, and evaluated participants’ willingness to share contact information and LinkedIn profiles. Data were collected through surveys (n = 91) and focus groups (n = 11) with young people aged 15–29 from across Canada who completed an RBC Future Launch-funded program. Findings indicated that branding and messaging in recruitment emails influenced response rates. Despite concerns about survey length, 97% of participants completed it, with most finishing within 15 min. Among the incentives offered, a CAD 10 payment resulted in the highest response rate. Additionally, both the CAD 10 incentive and the LinkedIn Learning licenses increased participants’ willingness to share LinkedIn profiles. The pilot study provided valuable insights into optimizing recruitment, survey design, and incentive structures for a longitudinal study. These findings provide insights for improving participant engagement and retention in research studies, as well as a co-creation approach to research design.
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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 | 0.324 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".