Socioeconomic marginalization and overdose : implications for upstream approaches for overdose prevention during a prolonged public health emergency
Bibliographic record
Abstract
Background: Canada and the United States are in the midst of an escalating overdose crisis. While poverty and socioeconomic disadvantage are increasingly recognized as important contributors to drug-related harm, their precise role in overdose remains understudied. This dissertation sought to: synthesize evidence regarding the pathways and mechanisms linking socioeconomic elements to overdose outcomes; examine the relationship between a comprehensive range of socioeconomic indicators and non-fatal overdose; identify and characterize gender-stratified profiles of socioeconomic exposures in men and women who use drugs; and, finally, determine whether these gender-stratified profiles predict non-fatal overdose. Methods: Data from Chapter 2 came from a realist review of research published between 2004 and 2019. Data for Chapters 3-5 cover the time period between 2014 and 2018 and are from two community-recruited prospective cohort studies of people who use drugs (PWUD) in Vancouver, Canada. A range of longitudinal analytic techniques were used, including: generalized linear mixed-effect models, repeated measures latent class analyses, and generalized estimating equations. Results: Findings from the realist review identified eight overlapping socioeconomic dimensions with documented linkages to overdose outcomes through material, normative, and bio-psychosocial pathways. In Chapter 3, across the total sample of PWUD from Vancouver, homelessness, lower material security, and participation in informal and illegal income generating activities were independently and positively associated with non-fatal overdose. In Chapter 4, gender-stratified analyses revealed that men and women experienced mutually reinforcing and overlapping socioeconomic exposures characterized by variations in income, material and housing security, participation in informal or illegal income generation, criminal justice involvement, and police contact. Gendered profiles of increasing socioeconomic disadvantage aligned with high-intensity drug use patterns (e.g., opioids and stimulants) and a range of health-related outcomes (e.g., HCV). In Chapter 5, exposure to multiple increasing dimensions of socioeconomic disadvantage was found to be independently associated with greater likelihood of experiencing non-fatal overdose in both men and women. Conclusions: Socioeconomic determinants are key drivers of overdose risk. To address the socioeconomic production of overdose risk, findings point to the urgent need to expand upstream and multilevel programs and policies, including inclusive, gender-informed health and social welfare programming, and broader drug policy reform.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".