The influence of social capital on drug use-related health behaviours: A study of marginalized drug users
Bibliographic record
Abstract
Marginalized drug users such as injection drug users and crack smokers are at risk for several health problems, including blood-borne disease infections such as HIV, Hepatitis B and C, and overdose. Behaviours that contribute to health risk stem not only from an individual's knowledge and beliefs, but are also shaped by processes of influence and constraint operating within networks of social relationships. The analysis of users' social network relationships is therefore important to understand users' engagement in drug use-related risk and protective behaviours. This study examined the influence of social network relationships on the drug use-related health behaviours of a convenience sample of 80 drug users (40 injection drug users and 40 crack smokers) in Toronto, Ontario, Canada. In the study, social network relationships were conceptualized as social capital. A comprehensive approach to social capital was incorporated which examined access to and use of social capital, as well as the influence of two dimensions of social capital, network structure and network resources, on a variety of drug use-related risk and protective health behaviours. Furthermore, differences between injection drug users (IDUs) and crack smokers (CSs) were examined to determine if these groups have differential health service needs. Quantitative and qualitative research methods were utilized in the study to explore these relationships. The study identified interesting relationships between various forms of social capital and different drug use-related health behaviours. The study showed that particular types of resources embedded in participants' drug use networks had a positive effect on engagement in protective health behaviours, while certain aspects of network structure within the drug network had a positive effect on engagement in health risk behaviours. Findings showed that resources embedded in the non-drug use network had a negative effect on engagement in protective health behaviours. The study also detected significant differences between IDUs and CSs with respect to levels of social capital and effect on drug use-related health behaviours. Several individual- and structural-level factors were also found to be associated with engagement in drug use-related health behaviours.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".