Characteristics and contributions of non-kin carers of older people: a closer look at friends and neighbours
Bibliographic record
Abstract
ABSTRACT Research on informal care-giving has largely neglected the contributions of non-kin carers. This paper investigated the characteristics and contributions of non-kin who care for older adults with a long-term health problem, and investigated friends and neighbours as distinct categories of care providers. Using data from 324 non-kin carers in the 1996 General Social Survey of Canada, this study compared individual and relationship characteristics, care tasks and amount of care provided for the two groups. Interpersonal and socio-demographic characteristics were investigated as mediators of potential differences between friends and neighbours in patterns of care. Results demonstrate that friend and neighbour carers differed on age, marital status, geographical proximity and relationship closeness. Friends were more likely than neighbours to assist with personal care, bills and banking, and transportation. Neighbours were more likely to assist with home maintenance. Friends provided assistance with a greater number of tasks and provided more hours of care per week, suggesting a more prominent role in the care of non-kin than neighbours. Age, income, a minor child in the household, proximity and relationship closeness significantly predicted amount of care provided, and relationship closeness largely explained differences between friends and neighbours. Future research on informal care-giving can build on the findings that distinguish friend and neighbour carers to further discriminate the dynamics of non-kin care.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".