Impacts of the COVID-19 pandemic on carer-employees’ well-being: a twelve-country comparison
Bibliographic record
Abstract
The aim of this analysis is to assess the potential ways that the COVID-19 pandemic has impacted Canadian carer-employees (CEs) and identify the needs CEs feel is required for them to continue providing care. We assess the similarities and differences in the stresses CEs faced during COVID-19 globally across countries in the G7, Australia, Spain, Brazil, Taiwan, India, and China. We aim to compare Canada against global trends with respect to the challenges of the COVID-19 pandemic, as well as the supports in place for CEs. The study utilized 2020 Carer Well-Being Index at the country level. Descriptive data on Canadian CEs is first reviewed, followed by comparisons, by country, on responses relating to: (a) time spent caring; (b) sources of support; (c) impact on paid work and career, and; (d) emotional/mental, financial, and physical health. The relationship between government support and emotional/mental health is also explored. When compared to pre-pandemic times, CEs in Canada on average spent more time caregiving, with 34% reporting more difficulty balancing their paid job and caring responsibilities. Seventy-one percent of CEs feel their mental health has deteriorated. Thirty-four percent of Canadian CEs received support from the government, and only 30% received support from their employers. Globally, there was a similar trend, with CEs experiencing deteriorating mental health, work impacts, and unmet needs during the pandemic. Comparing the well-being of Canadian CEs with other countries provides an opportunity to evaluate areas where Canadian policies and programs have been effective, as well as areas needing improvement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".