Street intercept method: An innovative approach to recruiting young adult high‐risk drinkers
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Many young adults are risky drinkers who are often missed by general population surveys. The aim of the present study was to assess factors affecting participation rates in a street intercept approach to recruiting young adult bar-goers for an online survey. DESIGN AND METHODS: Two hundred eighty-seven young adults were approached as they entered the bar district of a medium-sized city on two consecutive weekend nights. Of these, 170 met eligibility requirements and were invited to complete a 2 min street survey for which they were paid $5 and given a gift card for $50 or $100 to be redeemed when they completed a follow-up online survey. RESULTS: Sixty-one per cent of eligible persons (n = 104) participated in the street survey, with greater participation on the second night (74% vs. 50%). Sixty-eight per cent (n = 71) of those who participated in the street survey completed the online survey, with no differences in response by age or student status; however, men were significantly more likely to complete the online survey if they received the higher incentive, had consumed less alcohol and were recruited before midnight. The larger incentive was especially effective at increasing completion rates for men who had consumed a larger amount of alcohol. DISCUSSION AND CONCLUSIONS: Street intercept is an effective and efficient recruitment method that can measure both drinking and other experiences in the event and link these data to information collected in follow-up research. Unlike recruitment through convenience samples, response rates and response bias can also be assessed.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 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".