FAMILY CAREGIVING AND CHANGE OF RETIREMENT PLAN AMONG CANADIAN FAMILY CAREGIVERS
Bibliographic record
Abstract
As one critical consequence of family caregiving for aging people, the literature of its impact on retirement is growing. Evidence has shown that considerable number of family caregivers changes their retirement plan to better perform caregiving tasks, but more work is needed to explore this relationship. Current study, based on the General Social Survey 2012 (Cycle-26)- Caregiving and Care Receiving, tries to build more knowledge of the effect of family caregiving on retirement planning. As a result, about 11% family caregivers change their retirement time due to caregiving responsibilities. The result of binary logistic regression (with standardized weight) shows that, when controlling the demographic information of both caregiver and care recipients, family caregivers with higher level of life accommodation (e.g. less time with children and spouse, adjustment in leisure activities and social participation, etc.), work accommodation (e.g. reduce working hours, take extra un-paid leave, etc.) and caregiving intensity are more likely to make change of retirement plan. Among those caregivers who change their retirement plan, about 57% retire earlier than their expectation, and 43% retire later. Results of binary logistic regression (with standardized weight) indicate that family caregivers who are female, with lower level of education and personal income, less workplace support are more likely to retire earlier than expectation. The findings of current study emphasize the importance to support the family caregivers with risk of retiring earlier, since retire earlier may increase their life burden in the aspects of financial competency and social activity.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".