Burden on Caregivers of Adults with Multiple Chronic Conditions: Intersectionality of Age, Gender, Education level, Employment Status, and Impact on Social Life
Bibliographic record
Abstract
Intersectionality analysis is the study of overlapping or intersecting social identities. Intersecting social identities may have an impact on the perception of burden by family caregivers of older persons with multiple chronic conditions (MCC). The purpose of this study was to explore the interaction of social factors on the burden of caring for older adults with MCC. A total of 194 caregivers of older adults with MCC were recruited from Alberta and Ontario. Survey data were collected at two time points, six months apart. Additive and multiplicative models were analysed using a generalised linear model to determine the level of caregiver burden. Medium-high social interference (impact on social life) was associated with higher burden when adjusted for age, gender, education, and employment status. The overall results of the five-way interaction suggest that males in general had lower burden scores than females. Irrespective of their education and employment status, females had generally higher burden scores. These results add to the current body of literature, suggesting areas for further research to fill knowledge gaps, and promoting ideas for evidence-guided public health interventions that focus on caregivers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".