Theorizing Precarization and Racialization as Social Determinants of Health: A Case Study Investigating Work in Long-Term Residential Care
Bibliographic record
Abstract
This thesis uses anti-racist and feminist political economy of health perspectives that intersect with immigrant status, in order to analyze the findings from a single-case study investigating the social determinants of health and work precarization in a residential long-term care (LTC) facility in Toronto, Ontario. Throughout this dissertation, I use mixed methods case study to investigate social, political, and economic implications in the lives of health care workers. Observation, interview, and survey methods were utilized to investigate workers health in relation to the precarization of work. Specifically, I used the concept of precarization as a lens to track the ways in which work relations impact the other social determinants of health. The main areas of focus include the intersections of gender, work, and occupational health with race, immigrant status, and culture; the ways in which precarization affects employees in this specific health care sector; the implications of precarization in the health and wellbeing of workers and their families; the role of (un)paid care work and social support provided by family members; and the exercise of strength, resilience, resistance, agency, and coping strategies. Broadly, I will argue that precarization in LTC is an increasingly experienced phenomenon, and that various levels of precarization are experienced by particular workers who are women, racialized persons, and immigrants. This study contributes to our understanding of racialization as a social determinant of health, and analyzes the health impacts of workplace inequality through the lens of precarization. The study makes the case for closer attention to racism and precarity both on and as social determinants of health.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.017 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".