Geographic distance and social isolation among family care-givers providing care to older adults in Canada
Bibliographic record
Abstract
Abstract Family care-giving is associated with social isolation, which can lead to adverse health and wellbeing outcomes among family care-givers. The role of geographic distance in care-giver social isolation (CSI) is unclear and has received mixed research findings. Framed by the Ecological Model of Caregiver Isolation, this study examined the relationship between geographic distance and CSI, including the interaction between geographic distance and care-giving intensity for CSI. Linear regression and analysis of covariance were used to test these hypotheses using a sub-set of family care-givers from the 2012 Canadian General Social Survey (N = 2,881). Care-givers living a short distance from receivers reported lower levels of social isolation than co-resident, moderate-distance and long-distance care-givers. Being involved in higher-intensity care-giving as the primary care-giver, undertaking more care-giving tasks and providing care more frequently resulted in higher CSI scores. Long- and moderate-distance care-givers reported greater CSI than co-resident and short-distance care-givers only when providing higher-intensity care-giving. Employing a granulated measure of geographic distance positioned within an ecological framework facilitates an understanding of the nuanced association between geographic proximity and CSI. Furthermore, the identified interaction effects between geographic distance and care-giving intensity on CSI further explicate the complexity of care-giving experiences. The findings are relevant for programmes supporting care-givers in different contexts, especially distance care-givers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".