The impact of the COVID-19 pandemic on social determinants of health, mental health, and substance use among key populations affected by sexually transmitted and blood-borne infections in Canada
Bibliographic record
Abstract
OBJECTIVES: We assessed the impact of the COVID-19 pandemic on social determinants of health, mental health, substance use, and access to mental health and harm reduction services among key populations disproportionately impacted by sexually transmitted and blood-borne infections (STBBI). METHODS: Online surveys (2021‒2022) were conducted in Canada among people who use drugs or alcohol (PWUD); African, Caribbean, and Black people (ACB); and First Nations, Inuit, and Métis peoples (FNIM). Descriptive analyses were conducted on social determinants of health, substance use, and access to services, stratified by changes in mental health status since the start of the pandemic. RESULTS: A total of 3773 participants (1034 PWUD, 1556 ACB, and 1183 FNIM) completed the surveys, with 45.6% reporting a major/moderate impact of the pandemic on their ability to pay bills and 53% experiencing food insecurity since the start of the pandemic. Half (49.4%) of participants reported deteriorating mental health. A higher increase in substance use and related behaviours was seen in those with worsening mental health. Among those using substances, two thirds (69.4%) of those with worsening mental health reported increasing their use of substances alone, compared to 46.9% of those with better/similar mental health. Access to mental health and harm reduction services was low. CONCLUSION: These intersecting health issues are among the risk factors for STBBI acquisition and act as barriers to care. Equitable interventions and policies addressing downstream and upstream determinants of health, with meaningful and sustainable leadership from key populations, may improve their health and well-being, to lower STBBI impact and improve future pandemic responses.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | medium |
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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".