Characterizing complex care relationships in Canada using the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
INTRODUCTION: Most caregiving research and policy has focused on dyadic, unidirectional relationships with one caregiver and one care receiver despite evidence for more complex caring relationships. OBJECTIVE: To describe caregivers and care receivers' relationships, with a focus on non-dyadic relationships, using baseline data from the Canadian Longitudinal Study on Aging (CLSA). METHODS: In this cross-sectional study, we analyzed unweighted baseline CLSA data to describe different self-reported care relationships. Care relationships of interest included: a) spousal reciprocal relationships, b) care chains, where one person receives and gives care with separate individuals, c) care caravans, where one person receives care from multiple caregivers, and d) compound caregivers, where one person provides care to multiple care receivers. We estimated frequencies for categorical variables, and either means and standard deviations or medians and interquartile ranges for continuous variables. RESULTS: Our sample included 51,338 CLSA participants. Of these respondents, 39% identified as a caregiver (mean age 62, 54% women), 6.5% identified as a care receiver (mean age 65, 58% women), and 5.3% identified as both a caregiver and care receiver (mean age 63, 65% women). Our research showed that 40% of caregivers reported giving care to 2 or more receivers and 52% of care receivers reported receiving care from 2 or more caregivers. Individuals both receiving and giving care demonstrated the following relationships: compound care chains (73%), care chains (19%), and reciprocal spousal care (4.6%). Gender characterization showed women had a wider range of care partners and were more likely to identify as caregivers and care receivers. While spouse and first degree relatives were the most commonly reported caregivers, friends and others were a large proportion of reported caregivers. CONCLUSION: We found that a large proportion of caregiving relationships were best described as a network that frequently included multiple caregivers, multiple care receivers, and some in both roles, as well as non-kin and extended relative participants. This characterization of care relationships allows us to better understand the needs of older adults and their caregivers as the population ages and care shifts towards aging-in-place strategies.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".