CARP- A New Vision of Aging for Canada | www.CARP.ca | 1-888-363-2279 March 2014 CARP’s New Vision for Caregiver Support
Bibliographic record
Abstract
Over 8 million informal caregivers in Canada provide care to family members or friends with chronic conditions, disabilities, and other health needs. Informal caregivers are unpaid caregivers that provide critical support and care that allow Canadians to recover from illness and age at home. The economic contribution of informal caregivers is conservatively estimated at $25-26 billion annually, taking into consideration the number hours of care provided and market wagesi. The savings to Canadian health care systems are even greater since many people who would otherwise need care provided by hospitals and other care facilities receive care at home instead. Despite the support provided to family or friends and the savings to the health care system, caregivers face a variety of challenges, ranging from lost work and income to physical and mental burdens. Caregivers are unlikely to have flexible work hours or arrangements or even unpaid job protection. Most caregivers do not receive financial support, adequate training, and support required to provide care. Many have to face the difficult choice between providing care to a loved one and leaving the labour force altogether, especially those providing heavy care, which is defined as providing 20 or more hours of caregiving each weekii. CARP calls on governments to take a comprehensive approach to providing greater supports for caregivers, recognizing the value caregivers provide to family and friends and the
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.147 | 0.039 |
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".