The Structural Burden of Caregiving: Shared Challenges in the United States and Canada
Bibliographic record
Abstract
In contrasting health care structures, we each served as caregivers to elderly parents where a shared and unexpected theme in our experiences was the substantial burden of negotiating and managing long-term care (LTC) services within our respective health and social care systems. In this article, we introduce and elucidate an under recognized source of caregiver burden in the United States and Canada: the structural burden of caregiving. We draw on shared and unique experiences cross-nationally, along with the literature, to illustrate that (a) today's caregiving is increasingly characterized by interactions with formal health and social systems in negotiating and managing services, (b) these systems are hampered by discontinuous and fragmented care which increase caregiver stress, and (c) this structural burden likely exacerbates inequity for both care recipients and caregivers. In conclusion, we call for theoretical models of caregiving to highlight health and social systems as creating burden and for measurement of caregiver burden to explicitly consider the time and stress stemming from interactions with formal health and social systems. Finally, we call for future policy evaluation to incorporate structural burden as an additional outcome in considering changes to LTC provisions and funding.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.028 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".