Impacts of care‐giving and sources of support: a comparison of end‐of‐life and non‐end‐of‐life caregivers in <scp>C</scp> anada
Bibliographic record
Abstract
This is the second in a series of papers that deal with care-giving in Canada, as based on data available from the Canadian General Social Survey (2007). Building on the first paper, which reviewed the differences between short-term, long-term and end-of-life (EOL) caregivers, this paper uniquely examines the caregiver supports employed by EOL caregivers when compared to non-EOL caregivers (short-term and long-term caregivers combined). Both papers employ data from Statistics Canada's General Social Survey (GSS Cycle 21: 2007). The GSS includes three modules, where respondents were asked questions about the unpaid home care assistance that they had provided in the last 12 months to someone at EOL or with either a long-term health condition or a physical limitation. The objective of this research paper was to investigate the link between the impact of the care-giving experience and the caregiver supports received, while also examining the differences in these across EOL and non-EOL caregivers. By way of factor analysis and regression modelling, we examine differences between two types of caregivers: (i) EOL and (ii) non-EOL caregivers. The study revealed that with respect to socio-demographic characteristics, health outcomes and caregiver supports, EOL caregivers were consistently worse off. This suggests that although all non-EOL caregivers are experiencing negative impacts from their care-giving role, comparatively greater supports are needed for EOL caregivers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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".