Recruitment of Community College Students Into a Web-Assisted Tobacco Intervention Study
Bibliographic record
Abstract
BACKGROUND: United States college students, particularly those attending community colleges, have higher smoking rates than the national average. Recruitment of such smokers into research studies has not been studied in depth, despite a moderate amount information on study recruitment success with smokers from traditional four-year colleges. Recruitment channels and success are evolving as technology evolves, so it is important to understand how to best target, implement, and evaluate recruitment strategies. OBJECTIVE: The aim of this paper is to both qualitatively and quantitatively explore recruitment channels (eg, mass email, in-person referral, posted materials) and their success with enrollment into a Web-Assisted Tobacco Intervention study in this priority population of underserved and understudied smokers. METHODS: Qualitative research methods included key informant interviews (n=18) and four focus groups (n=37). Quantitative research methods included observed online responsiveness to any channel (n=10,914), responses from those completing online screening and study consent (n=2696), and responses to a baseline questionnaire from the fully enrolled study participants (n=1452). RESULTS: Qualitative results prior to recruitment provided insights regarding the selection of a variety of recruitment channels proposed to be successful, and provided context for the unique attributes of the study sample. Quantitative analysis of self-reported channels used to engage with students, and to enroll participants into the study, revealed the relative utilization of channels at several recruitment points. The use of mass emails to the student body was reported by the final sample as the most influential channel, accounting for 60.54% (879/1452) of the total enrolled sample. CONCLUSIONS: Relative channel efficiency was analyzed across a wide variety of channels. One primary channel (mass emails) and a small number of secondary channels (including college websites and learning management systems) accounted for most of the recruitment success. TRIAL REGISTRATION: ClinicalTrials.gov NCT01692730; https://clinicaltrials.gov/ct2/show/NCT01692730 (Archived by WebCite at http://www.webcitation.org/6qEcFQN9Q).
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".