Co-occurring Issues Facing Patients Who Use Unregulated Drugs: Insights From a Survey in Edmonton, Canada
Bibliographic record
Abstract
Context The COVID-19 pandemic and ongoing unregulated drug poisoning emergency have dramatically increased morbidity and mortality in major urban centres across the US and Canada. Objective The objective of this study was to characterize the substance use patterns of people who use drugs (PWUD), assess the rates of co-occurring conditions and examine the health service needs of this population. Study Design Cross-sectional study using a community-based survey. Data was analyzed using descriptive statistics. Setting or Dataset Participants were recruited from community organizations in central Edmonton, Canada to participate in interviewer-administered surveys from April to September 2023. Population Studied 499 structurally vulnerable PWUD, defined as engaging in regular use of currently illegal drugs at least once a month and spending time in Edmonton9s inner city. Intervention/Instrument Survey questions focused on socio-demographic information, substance use patterns, health status, use of treatment and harm reduction services, and acceptability of emerging services. Participants received a CA$30 cash honorarium for their time. Outcome Measures N/A Results 65% (324/499) of participants identified as men, and the average age was 44. 69% (343/499) of participants identified as Indigenous, and a majority of participants (80%, 401/499) did not currently have housing. 88% (440/499) reported having witnessed a drug poisoning/overdose in the previous 6 months, and 75% (376/499) reported having lost someone they cared about to a drug poisoning. Of those who reported losing someone they cared about, 81% (304/376) said they have lost more people due to poisonings since the start of the COVID-19 pandemic. 76% (379/499) of participants expressed having a diagnosed or undiagnosed serious mental health problem, such as depression, anxiety, post-traumatic stress disorder, bipolar disorder, or schizophrenia. 40% (201/499) of participants were reluctant to seek medical care because they use drugs. Conclusions There is a substantial rate of co-occurring houselessness and mental illness among PWUD in central Edmonton. This is compounded by both widespread grief from the loss of loved ones to a highly potent and often contaminated drug supply, and reluctance to seek out medical care. These results point to the importance of using a trauma-informed lens to address co-occurring mental health conditions and structural vulnerability among patients who use drugs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".