Informal Caregiving and Its Hidden Cost to National Economy- With a Toronto Case Study
Bibliographic record
Abstract
More than eight million Canadians are providing care for their aging family members, relatives, neighbours, or friends. Due to staff shortage, eldercare facilities are also relying on their residents’ families to fill the gap of the care needs. Caregiving responsibilities have forced many employees to take time-off from work or take early retirement, which is a heavy loss of productivity of Canada’s national economy. This study employed a mixed method strategy, and with both qualitative and quantitative data collection methods: interview, focus group discussion, and a questionnaire survey. It shows that Canadians must take time off from work or to leave jobs for providing care to their loved ones, even when they are residing in a long-term care setting. This seemingly private matter is a very public one in the other side of the coin: family caregivers’ lost time in employment is affecting Canada’s national economy significantly. Government should work with stakeholders to develop a national strategy to tackle the issue. COVID-19 outbreak has revealed long-term care institutions’ struggle with severe staff shortage in Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.021 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".