Hidden risks: Navigating structural barriers during COVID-19 pandemic lockdowns in the Greater Toronto Area
Bibliographic record
Abstract
The COVID-19 pandemic exposed and exacerbated the many structural inequalities embedded in Canadian society. It is critical to understand how Canadians experienced the early COVID-19 pandemic and its associated public health strategies around risk mitigation (such as lockdowns), to assess how structural drivers of health may lead to the replication and embodiment of health inequalities. This dissertation uses an anthropological lens to investigate the upstream forces that shape risk and potential health outcomes during the COVID-19 pandemic in three populations most affected by pandemic lockdowns. The first paper of this dissertation used reflexive thematic analysis to explore the economic and occupational experiences of racialized and/or immigrant people in Peel Region, Ontario throughout 2020 – 2021. Job loss led to a layering of risks for many participants, best examined through a biocultural risk focusing model. Results also demonstrated the unique situation of Peel Region, and thus highlighted the need for unique, additional supports for the region during lockdowns. The second paper of this dissertation used reflexive thematic analysis to explore the experiences of racialized young adults (aged 18 – 29) in Peel Region throughout 2020 - 2021. Lockdowns placed many participants’ lives into a period of stasis, with resultant mental health effects. Rather than mental health supports, participants instead sought and benefitted from the support of their families. Intersectionality frameworks highlighted the unique risks faced by racialized young adults with intersecting, marginalized identities in this study. The third and final paper of this dissertation used an evolutionary lens to examine the upstream forces that shape risk perception in University of Toronto students from 2021 – 2022. Mixed-methods analysis demonstrated that increased perceived vulnerability to disease was associated with students’ perceiving campus situations as unsafe; furthermore, thematic analysis noted a shifting attitude towards public health protocols as the pandemic lockdowns continued. The COVID-19 pandemic is still unfolding; thus, its long-term effects on people’s health and well-being have yet to be elucidated. It is only through the understanding of these structural drivers that we can reduce or mitigate harm within our society.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".