Self-reported health impacts of caregiving by age and income among participants of the Canadian 2012 General Social Survey
Bibliographic record
Abstract
INTRODUCTION: Increases in life expectancy and the underlying age structure of the Canadian population have contributed to dramatic increases in the number of seniors who are caregivers. While caregiving is associated with several adverse health impacts, there is a need to better understand how these impacts might be different among older caregivers, and whether those impacts are modified by socioeconomic status. METHODS: We sought to address these research gaps by using cross-sectional data provided by participants of the 2012 Canadian General Social Survey (GSS). Descriptive analyses were performed to compare the self-reported health impacts that participants attributed to caregiving, and how these varied by age and income. Logistic regression analyses were performed to identify which factors were associated with self-reported impacts on overall health among caregivers 65 years of age and older. RESULTS: The demographic characteristics of the care-providers varied substantially by age with older caregivers having lower incomes and devoting more time to caregiving relative to those who were younger. The self-reported impacts of caregiving on overall health were greatest among those between the ages of 35 and 64, and this pattern was evident across all income groups. Feelings of loneliness and social isolation as a result of caregiving responsibilities appeared to be mitigated by both greater age and income. However, across all age groups, caregiving was more likely to adversely impact exercise habits, healthy eating, and alcohol consumption than to promote more positive behaviours. CONCLUSION: Providing care impacts health behaviours and mental health regardless of age and income. However, our findings suggest that older caregivers (who are most often women)-who provide the most hours of care and on reduced incomes relative to younger caregivers-appear less impacted in terms of health behaviours, perhaps as a result of fewer competing demands relative to younger caregivers. Taken together, these findings suggest that support systems must consider caregiver impacts that vary in complex ways across age, sex, and income.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".