A comparative analysis of carer-employees in Canada over time: a cross-sectional analysis of Canada’s General Social Survey, 2012 and 2018
Bibliographic record
Abstract
OBJECTIVES: The aims of the study are to identify trends in the socio-demographic, health, and work profiles of Canadian carer-employees (CEs) over time, as well as the gender difference in the intensity of caring. METHODS: Cross-sectional data from cycles 26 and 32, collected in 2012 and 2018 respectively, of the Canadian General Social Survey (GSS) were used. Logistic, multinomial logistic, and linear regressions were used to estimate how caregiving is associated with caregivers' health, well-being, and work in both cycles. Regressions from both cycles were then compared with chi-square tests for significant differences over time. RESULTS: The proportion of male CEs grew between 2012 and 2018, and women were no longer more likely to be a CE. The intensity of care for female CEs was significantly increased from 2012 to 2018 as compared with their male counterparts. General health (2018: OR = 0.25[0.11, 0.61] vs. 2012: OR = 0.33[0.15, 0.72]) and life satisfaction ([Formula: see text] = -0.42[0.54, -0.30] vs. [Formula: see text] = -0.22[-0.30, -0.14]) were significantly worsened with respect to the role of CEs from 2012 to 2018. CONCLUSION: Our study provides the evidence that CEs' health and well-being have worsened over time, especially for female CEs, indicating that the needs of CEs are growing at a faster rate than the supports available. The results are meaningful in informing and justifying the provision of CE supports at work in order to sustain CEs in the workplace, such as the carer-friendly workplace policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".