Bibliographic record
Abstract
Little is known about how current conditions in the Canadian home care sector (e.g., COVID-19 pandemic) are imposing financial risks on unpaid caregivers. Financial risk considers the sources and magnitude of direct, out-of-pocket care expenditure, and corresponding impacts across domains of financial risk (income-generating potential, employment, and personal health). Pragmatically, financial risks from caregiving call into question whether the universal dimensions of social welfare built on universality that are characteristic of Canada’s Medicare system can be applied to home care. Using Ontario as a case-study, this dissertation explores the financial risks of unpaid, homebased caregiving through the lens of welfare regime theory. We conducted an original, cross-sectional survey to explore the financial risks of unpaid caregiving (Chapter 3). This survey, completed by 190 caregivers between August - December 2020, revealed challenges paying for care expenses such as supplemental support from a personal support worker (PSW), and difficulties balancing employment compromising income-earning potential (Chapter 4). We also conducted interviews with 24 dementia caregivers across Ontario to understand how challenges acquiring a PSW have contributed to financial risk (Chapter 5). Findings suggest dementia caregiving requires substantial hours of PSW care to ensure unpaid caregivers can maintain employment or take a break (respite). However, caregivers felt they were not granted enough subsidized hours from the state. This meant working caregivers took time off work to provide care, thereby contributing to lost wages. Other caregivers paid out-of-pocket for additional hours of privately acquired home care, which contributed to experiences of financial risk. Financial risks observed in Chapters 4 and 5 raise important questions about how social benefits ought to be distributed in the context of home care to protect unpaid caregivers. Drawing on notions of universalism in post-modern welfare regimes, including inclusive citizenship and new social risks, we developed an adapted welfare regime typology to better describe the organization of social benefits for unpaid, homebased caregivers (Chapter 6). This typology reveals home care follows a liberal ethos of social benefit distribution. Findings from this dissertation may better inform policies and programs that aim to protect unpaid caregivers from financial risk across different welfare states.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".