Gender and sex differences in carers’ health, burden and work outcomes: Canadian carers of community-dwelling older people with multiple chronic conditions
Bibliographic record
Abstract
Using two waves of survey data on family carers caring for older adults with multiple chronic conditions in Ontario and Alberta, this article provides a sex and gender analysis of 194 carers’ health outcomes. Gender and sex differences were examined on the following health outcomes: general self-efficacy; physical and mental health composite scores; overall quality of life; and the Zarit Burden Inventory – as well as experiences with work interference for carer-employees. Multivariate ordinary least squares linear regressions were used to estimate the effects of sex and gender, controlling for the carer’s socio-demographic and geographic characteristics, as well as for the characteristics of the care recipients. Sex and gender were found to have differentiated effects on each health outcome examined, providing evidence for specifically targeting health interventions by sex and gender. First, sex matters, as illustrated by the fact that female carers were found to be experiencing more negative health impacts than male carers (shown in the physical composite score and the quality of life score). This suggests that health-related interventions need to be targeted at female carers. Further, male carers are more likely to experience less carer burden, and more work interference, than female carers. Second, gender matters, as illustrated by the fact that masculine and androgynous genders showed significantly positive associations with general self-efficacy. This suggests that carers with feminine and undifferentiated gender roles experience more challenges with general-self-efficacy and could benefit from training and educational interventions to enhance their confidence in the caring role.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".