The Intersection of Gender, Culture and Society for Caregivers of Older Adults Ageing in Place in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: It is reported that women are more likely to be caregivers than men, experience a higher burden of care and increased emotional health sequelae as a result. Social location (a person's gender, culture, ethnicity, etc.) is known to influence caregiving experiences. However, there is limited work that draws attention to how cultural and linguistic diversity shapes the experiences and expectations of informal caregivers. OBJECTIVE: The authors aimed to study how to reallocate health and social service resources to better support older adults ageing in place. However, some participants felt strongly about the role of gender. This report addresses the gap for better understanding (1) how gender influences informal caregiving for older adults ageing at home in Ontario, Canada, and (2) how culture may influence gendered caregiving expectations for this population. DESIGN: A critical social justice paradigm and balance of care framework guided the research. Focus groups (15) and one-one-one interviews (7) were carried out. A collaborative approach to codebook thematic analysis was conducted. SETTING AND PARTICIPANTS: This study was carried out in Peel, a diverse region in Ontario, Canada. 42 individuals participated in the study (14 older adults, 10 caregivers and 18 healthcare providers). FINDINGS: Four themes were found regarding the role of gender in caregiving: (1) women caregivers as catalysts for ageing in place, (2) gender norms, generational standards and the societal expectation for women to be caregivers, (3) the intersection of culture and gender on caregiving for older adults and (4) health service workforce as women-dominant and linguistically diverse. DISCUSSION AND CONCLUSION: Service needs not currently met by Canada's healthcare system often become absorbed by women caregivers who facilitate ageing in place. Further research is required to better understand: (1) how a larger breadth of communities experience the intersection of gender and culture in the care of older adults in Ontario, Canada, and (2) how to better harness the diversity within Canada's homecare workforce to allow for cultural, linguistic and/or gender alignment with older adult clients. PATIENT OR PUBLIC CONTRIBUTION: Patients and caregivers were research participants; however, the focus groups were co-design sessions, in which participants built and shaped personas and care packages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".