Bibliographic record
Abstract
As utilization of home care increases across Canada, questions are emerging concerning the extent to which home care expenses, many of which are not publicly covered, are borne by unpaid caregivers of home care recipients. In this systematic review, we review English-language literature published between 2001 and 2022 exploring the magnitude and sources of home care costs incurred by unpaid caregivers in Canada. Of particular interest were empirical, cost-of-illness studies that describe the implications of these costs across domains of financial risk, including caregivers’ income level, employment status, and personal health. Following the screening of 492 studies derived across 6 databases (OVID Medline, CINAHL, PsycINFO, AMED, EconLit, and EMBASE), 24 studies were included in this review. Overall, few studies describe how home care expenses incurred by unpaid caregivers contribute to their financial risk. While some studies characterize the direct costs of caregiving incurred by caregivers, including out-of-pocket expenditure on transportation to medical appointments, respite care, home renovations, supplemental housekeeping, and prescription medications, limited studies attempt to estimate the magnitude of these expenses. Concerning financial risk, the literature is chiefly concerned with indirect costs of caregiving, including consequences on caregivers’ employment (foregone wages). Findings from this literature review suggest further work is needed in Canadian context to document costs associated with unpaid home care provision.
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.030 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".