Exploring Help-Seeking Behaviours: Perspectives of Adult-Child Caregivers
Bibliographic record
Abstract
As life expectancies increase, so does the rate of older adults diagnosed with dementia; most commonly, in the form of Alzheimer’s disease. An Alzheimer’s disease diagnosis not only impacts the person afflicted, but also people around them who take on the role of caregiver. The role of caregiving often falls on the shoulders of adult-children, who are simultaneously balancing their own life commitments with offering high quality, around the clock care. Many elements impact one’s caregiving experience, including sociodemographic factors such as gender, age, occupation and culture. As the formal service sector works to improve access to support, it is critical to understand the help-seeking behaviours of adult-child caregivers; moreover, in a country like Canada with an abundance of diversity, variations in help-seeking behaviours must be recognized. The purpose of this study was to explore the help-seeking behaviours of adult-children, caring for a parent with Alzheimer’s disease. This qualitative study utilized a multiple case study approach, and was guided by the Anderson Socio-Behavioral Model of Health Service Utilization (1995). Findings from six adult-child caregivers from hyphenated cultural backgrounds suggest that there may be universality to caregiver’s desires and determinants of help-seeking behavior. However, in terms of programs designed for their illness-afflicted parent, themes of linguistic diversity, trauma-sensitivity and culture matching between care providers and service users were presented as important strategies to promote help-seeking behaviours and service utilization. Implications for social work theory, research, practice and policy are presented.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".