Web-Based Eligibility Quizzes to Verify Opioid Use and County Residence Among Rural Young Adults: Eligibility Screening Results from a Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Web-based methods can be used to collect data from hidden populations, including people who use drugs (PWUD). These methods might be especially advantageous among PWUD in rural areas, where transportation barriers are prevalent, stigma may heighten concerns about confidentiality, and internet access is improving. However, Web-based research with PWUD can be challenging, especially in verifying eligibility. Administering quizzes to verify residential and substance use eligibility could prove valuable in online research among PWUD, yet the utility of this approach is currently unknown. OBJECTIVE: This study describes the implementation of online eligibility quizzes about the local community to verify residence in the target study area along with drug dose, appearance, and price to verify opioid misuse. METHODS: To be eligible, individuals had to live in 1 of 5 eastern Kentucky counties, report using opioids to get high in the past 30 days, and be 18 to 35 years old. Participants recruited from August 2017 to July 2018 were asked questions about their opioid use followed by a quiz about drug dose, appearance, and price to verify substance use eligibility. Residential eligibility was verified with 5-question quizzes assessing knowledge of the county where they reported living. Questions tested knowledge about towns, festivals, and landmarks; local school mascots and colors; and presence of certain retail stores, restaurants, and facilities (eg, jails). A subsample that reported using opioids in the past 24 hours was randomly selected to complete urine drug testing (UDT). Nonparametric tests were performed to explore differences across demographic subgroups. RESULTS: Of the 410 entries assessed for eligibility, 39.3% (161/410) were ineligible as they reported no substance use, being outside the age range, or living outside the study area. Of the remaining 249 who met the eligibility criteria based on age, residency, and opioid misuse, 94.0% (234/249) passed the eligibility quizzes. Among those who passed the heroin quiz, 99.4% (167/168) recognized the image of powdered heroin, 94.6% (159/168) answered the cap size (ie, the purchase unit) question correctly, and 97.0% (163/168) answered the street price question correctly. Among those who passed the drug quiz for prescription opioids, 95% (36/38) answered the dose question correctly, and 82% (31/38) selected the correct image. In a random sample of participants who completed UDT within 3 days of their online screening, 74% (25/34) tested positive for an opioid. CONCLUSIONS: This study demonstrated the utility of using online eligibility screening quizzes to verify opioid misuse and residence. Participants accurately recognized heroin and prescription opioid doses, prices, and images and correctly answered questions about features of their county. Online quizzes to screen and enroll PWUD hold promise for future research as an alternative to more time- and resource-intensive approaches that could offset the advantages of Web-based methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".