HOME protocol for a national online survey of people who inject drugs
Bibliographic record
Abstract
Most surveys of people who inject drugs (PWID) fail to represent the full population of PWID, because usual recruitment methods do not achieve geographic and sociodemographic diversity. People of color, people residing in rural and/or harm reduction-deprived areas, and people who rarely connect with social services are the least surveyed and understood PWID populations. Online-based recruitment and surveys may better reach these hidden PWID populations than standard venue-based recruitment. As technology use and internet access become more ubiquitous, even for unstably housed populations, research using online-based recruitment and survey techniques are growing in the substance use field. These methods hold promise for obtaining larger and more diverse PWID samples, but there are no standards for using online recruitment and survey administration methods to reach large populations of PWID vulnerable to overdose and other threats. Best practices are needed to maximize data quality, prevent fraudulent responses, and minimize selection biases. The HOME (Harm reduction services Offered through Mail-delivery Expansion) study recruits and enrolls a national, online-recruited, longitudinal cohort of 1233 PWID and follows them for 18 months. Key objectives are to assess prior harm reduction utilization and future uptake of mail-based harm reduction services and retention in these services. We describe our online data collection protocol, including recruitment approaches, detecting fraud, maximizing data quality, and participant retention throughout follow-up. These strategies can inform subsequent large-scale, nationwide efforts that recruit PWID through the internet.
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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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.423 | 0.106 |
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".