Income Generating Activities of People Who Inject Drugs
Bibliographic record
Abstract
Background Injection drug users (IDU) commonly generate income through prohibited activities, such as drug dealing and sex trade work, which carry significant risk. However, little is known about the IDU who engage in such activities and the role of active drug use in perpetuating this behavior. Methods We evaluated factors associated with prohibited income generation among participants enrolled in the Vancouver Injection Drug Users Study (VIDUS) using logistic and linear regression. We also examined which sources of income respondents would eliminate if they did not require money to pay for drugs. Results Among 275 IDU, 145 (53%) reported engaging in prohibited income generating activities in the past 30 days. Sex work and drug dealing accounted for the greatest amount of income generated. Non-aboriginal females were the group most likely to report prohibited income generation. Other variables independently associated with prohibited income generation include daily heroin injection (AOR = 2.3) and daily use of crack cocaine (AOR = 3.5). Among these individuals, 68 (47%) indicated they would forgo these earnings if they did not require money for illegal drugs, with those engaged in sex trade work (62%) being most willing to give up their illegal source of income. Conclusion These findings suggest that the costs associated with illicit drugs are compelling IDU, particularly those possessing markers of higher intensity addiction, to engage in prohibited income generating activities. These findings also point to an opportunity to explore interventions that relieve the financial pressure of purchasing illegal drugs and reduce engagement in such activities, such as low threshold employment and expansion of prescription and substitution therapies.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".