HOW WELL DO INSTITUTIONALIZED ASSUMPTIONS ABOUT UNPAID CAREGIVERS MATCH THE EXPERIENCES OF HOME CARE CLIENTS?
Bibliographic record
Abstract
Abstract Despite efforts to acknowledge diversity among unpaid caregivers, research, advocacy, practice and policy tends to be based in, and to reproduce, normative expectations and assumptions about who provides unpaid care and what this support looks like. The objective of this analysis is to generate unique insights into unpaid caregiving by exploring the situations of older adults receiving publicly funded home care and their experiences of accessing unpaid support. The analysis draws on case study interviews with twelve home care clients, their unpaid caregiver, home care aide and case coordinator, conducted at three points in time, before and after the Covid-19 pandemic (129 interviews). Analysis indicated only one participant had access to a caregiver that aligned with normative conceptualizations. Rather, situations arose in which: a) family members grappled with physical or mental health challenges limiting their participation in care (sometimes meaning the client is themselves a caregiver, or the caregiver is also receiving home care services); b) caregivers facing burnout sought to delimit their participation in care; c) family members’ participation was limited by older adults’ reluctance to accept their help; d) caregivers were largely unavailable, unreliable, or peripheral; or e) client’s unpaid support networks were diffuse without a clearly identifiable ‘caregiver.’ Findings are used to nuance and problematize normative assumptions about caregivers. Discussion highlights the need for research, policy and programs related to unpaid caregiving to better reflect the lived realities of unpaid support for older adults and often overlooked sources of diversity in caregiver circumstances and roles.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 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".