The validity of reporting willingness to use a supervised injecting facility on subsequent program use among people who use injection drugs
Bibliographic record
Abstract
Background Innovative health programs for injection drug users (IDUs), such as supervised injecting facilities (SIFs), are often preceded by evaluations of IDUs’ willingness to use the service. The validity of these surveys has not been fully evaluated. We sought to determine whether measures of willingness collected prior to the opening of a Canadian SIF accurately predicted subsequent use of the program. Methods Data were derived from a prospective cohort of IDUs. The sample size for this study was 640 IDUs. Using multivariate logistic regression, it was assessed if a history of reporting willingness to use the program, were it available, was associated with subsequent use. In sub-analysis restricted to individuals who had a history of reported willingness, we used multivariate longitudinal analysis to identify factors associated with not attending the SIF. Results Among 442 IDUs, 72% of those who reported initial willingness to use a SIF later attended the program, and a prior willingness to use a SIF significantly predicted later attendance (adjusted odds ratio = 1.67). In sub-analyses restricted to those who had a history of reporting willingness to use the SIF, not using the program was predicted by not frequenting the neighborhood where the SIF was located. Conclusion Our findings indicate that reported willingness measures collected from IDUs regarding potential SIF program participation prior to its opening independently predicted later attendance even when variables that were likely determinants of willingness were adjusted for. These data suggest that willingness measures are reasonably valid tools for planning the delivery of health services among IDU populations.
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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".