The Financial Risks of Unpaid Caregiving During the COVID-19 Pandemic: Results From a Self-reported Survey in a Canadian Jurisdiction
Bibliographic record
Abstract
As health service delivery shifts from institutions to the home, greater care responsibilities are being imposed on unpaid caregivers. However, gaps remain concerning how these responsibilities are contributing to caregivers’ financial risk. This study describes results from an online survey conducted in late-2020 in Ontario, Canada, about the financial risks of unpaid, homebased caregiving throughout the first year of the COVID-19 pandemic. Among 190 caregivers, salient findings include difficulties paying for care expenses after the pandemic was declared than before ( P = .002); more caregivers retiring or becoming unemployed during the pandemic than before ( P = .013); and a significant relationship between paying out-of-pocket for a home care worker and experiencing a decrease in the availability of such support during the pandemic ( P = .029). Overall, the financial stressors of caregiving during the pandemic contributed negatively to caregivers’ mental health, with 64.2% noting could be partly offset by greater government and employment-based assistance in managing care expenses and productivity losses. Findings from this study will better inform policies that aim to protect unpaid caregivers from financial risk in pandemic recovery efforts and beyond. Results may also be useful in other welfare states where unpaid caregivers provide the majority of home care services.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".