Identifying the Most Effective Recruitment Strategy Using Financial Reimbursements for a Web-Based Peer Network Study With Young People Aged 16-18 Years: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Peers are an important determinant of health and well-being during late adolescence; however, there is limited quantitative research examining peer influence. Previous peer network research with adolescents faced methodological limitations and difficulties recruiting young people. OBJECTIVE: This study aims to determine whether a web-based peer network survey is effective at recruiting adolescent peer networks by comparing 2 strategies for reimbursement. METHODS: This study will use a 2-group randomized trial design to test the effectiveness of reimbursements for peer referral in a web-based cross-sectional peer network survey. Young people aged 16-18 years recruited through Instagram, Snapchat, and a survey panel will be randomized to receive either scaled group reimbursement (the experimental group) or fixed individual reimbursement (the control group). All participants will receive a reimbursement of Aus $5 (US $3.70) for their own survey completion. In the experimental group (scaled group reimbursement), all participants within a peer network will receive an additional Aus $5 (US $3.70) voucher for each referred participant who completes the study, up to a maximum total value of Aus $30 (US $22.20) per participant. In the control group (fixed individual reimbursement), participants will only be reimbursed for their own survey completion. Participants' peer networks are assessed during the survey by asking about their close friends. A unique survey link will be generated to share with the participant's nominated friends for the recruitment of secondary participants. Outcomes are the proportion of a participant's peer network and the number of referred peers who complete the survey. The required sample size is 306 primary participants. Using a multilevel logistic regression model, we will assess the effect of the reimbursement intervention on the proportion of primary participants' close friends who complete the survey. The secondary aim is to determine participant characteristics that are associated with successfully recruiting close friends. Young people aged 16-18 years were involved in the development of the study design through focus groups and interviews (n=26). RESULTS: Participant recruitment commenced in 2022. CONCLUSIONS: A longitudinal web-based social network study could provide important data on how social networks and their influence change over time. This trial aims to determine whether scaled group reimbursement can increase the number of peers referred. The outcomes of this trial will improve the recruitment of young people to web-based network studies of sensitive health issues. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44813.
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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 | 0.066 | 0.079 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.073 | 0.012 |
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".