IDENTIFYING THE PATTERNS OF CAREGIVING DEMANDS: A LATENT CLASS ANALYSIS APPROACH
Bibliographic record
Abstract
Abstract Caregiving demands comprise the care tasks being performed or assistance being provided. Previous studies have assessed the care demands as intensity and types of care provided using various measures such as the number of care tasks provided, the hours of care provided, the number of ADL/IADL they assisted with, and the number of people assisted. However, most caregiving literature has treated caregiving demands as one-dimensional rather than considering the multi-dimensional attributes of care demands. This study examines distinct patterns of caregiving demands, each formed by varied combinations of the hours spent providing care a week, how often care was provided, the location care provided, and whether care was provided as a primary caregiver. Based on the 2022 Caregiving, Aging, and Financial Experiences (CAFE) Study data, the latent class and regression analysis were conducted on a nationally representative sample of Canadian informal caregivers. We identified four distinct patterns of caregiving demands: excessive, moderate, manageable, and minor. Excessive and moderate patterns share the characteristic of providing in-home care as primary caregivers, but the excessive group has higher burden levels than the moderate pattern. Manageable and minor patterns show low levels of burden as secondary caregivers, but the minor group does not provide in-home care. These patterns differ in caregivers’ sociodemographic characteristics (caregiver age, gender, minority, education, living with a partner, working status, and relationship to a recipient). The findings provide the basis for further understanding how the multi-dimensional layers of care demand conjointly shape the patterns of caregiving demands.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".