Syndemic Connections: Overdose Death Crisis, Gender-Based Violence and COVID-19
Bibliographic record
Abstract
This article will use syndemic theory to identify and analyze overlapping health and social conditions, focusing specifically on how gender-based violence is systemically interconnected with contemporary public health issues. The overdose death crisis that continues to afflict Canadian populations is not an isolated health issue. Across Canada, it is intertwined with mental health, HIV/AIDS, COVID-19 and structural violence—the chronic and systemic disadvantages affecting those living in poverty and oppressive circumstances. Opioid use is an often-avoidant coping strategy for many experiencing the effects of trauma, relentless fear, pain, ill health and social exclusion. In particular, Indigenous and non-Indigenous women’s experiences with opioid addiction are entangled with encounters with gender based-violence, poverty and chronic ailments within structurally imposed processes and stressors shaped by a history of colonialism, ruptured lifeways and Western ways of knowing and doing, leading to disproportionate harms and occurrences of illness. While biomedical models of comorbidity and mortality approach substance misuse, gender-based violence and major infectious diseases such as HIV/AIDS and COVID-19 as distinct yet compounding realities, this article argues that these conditions are synergistically interrelated via the critical/reflexive lens of syndemic frameworks. Through secondary research using academic, media and policy sources from the past decade in Canada, complemented by prior ethnographic research, the synergistic connections among opioid addiction, gender-based violence and the effects of the COVID pandemic on diverse women will be shown to be driven by socio-structural determinants of health including poverty, intergenerational trauma, the legacy of colonialism and Western optics. Together, they embody a contemporary Canadian syndemic necessitating coordinated responses.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".