Structural violence and necropolitics among Indigenous Peoples living with HIV who use substances in the Prairie provinces during COVID-19
Bibliographic record
Abstract
BACKGROUND: Within Manitoba and Saskatchewan, pre-existing health inequities amongst Indigenous groups were intensified during the COVID-19 pandemic. Service disruptions in the health and social service sector-combined with the effects of intersectional stigma-disproportionately impacted Indigenous peoples living with HIV (IPLH). IPLH experience structural violence and necropolitical exclusion through systemic forms of stigma situated within Canada's expansive colonial history. Utilizing the theoretical foundations of structural violence and necropolitics, this qualitative study examines how the COVID-19 pandemic amplified preceding states of inequity for IPLH. METHODS: Semi-structured interviews were conducted with 60 participants. The sample comprised of those with lived experience (n = 45) as well as those who provided services for IPLH (n = 15). Indigenous Storywork guided the data collection and analysis process. Topics explored within each interview included access to health and social services, harm reduction, substance use, and experiences in providing services during COVID-19 pandemic. Thematic analysis was used to identify common themes throughout each story. RESULTS: Our results indicate that the COVID-19 pandemic exposed and amplified pre-existing forms of structural violence and necropolitical logics for IPLH within Manitoba and Saskatchewan. Specifically, we describe how structural violence and necropolitics are manifested via three main avenues- (i) restrictions and removal of care, (ii) bureaucracy and institutional care politics, and (iii) discrimination and systemic racism within the Canadian healthcare system. CONCLUSION: The COVID-19 pandemic within Manitoba and Saskatchewan sparked massive changes in service provision within settler-colonial and neoliberal institutions of care. For those services that remained open to IPLH, masking requirements, questionnaire requirements, scheduling requirements, and a lack of in-person services acted as only some of the barriers described by community members as detrimental to care access. Increased experiences of discrimination in health care on the basis of substance use or HIV status further limited access to needed services.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".