“Caregiving is like on the job training but nobody has the manual”: Canadian caregivers’ perceptions of their roles within the healthcare system
Bibliographic record
Abstract
Abstract Background Stepping into the role of an unpaid caregiver to offer help for a family member or friend is often considered a natural expectation of partners or family members. In Canada, the contributions of caregivers are substantial in healthcare provision but this comes at a considerable cost to the caregivers in both health and economic terms. Methods In this study, we conducted a secondary analysis of a collection of qualitative interviews with 39 caregivers of people with chronic physical illness to assess how they described their particular roles in caring for a loved one. We used a model of caregiving roles, originally proposed by Twigg in 1989, as a guide for our analysis, which specified three predominant roles for caregivers – as a resource, as a co-worker, and as a co-client. Results The caregivers in this collection spoke about their roles in ways that aligned well with these roles, but they also described tasks and activities that fit best with a fourth role of ‘care-coordinator’, which required that they assume an oversight role in coordinating care across institutions, care providers and often advocate for care in line with their expectations. For each of these types of roles, we have highlighted the limitations and challenges they described in their interviews. Conclusions We provide some examples of system-level policy and programs from different jurisdictions developed in recognition of the need to sustain caregivers in their role and respond to such limitations. We argue that a deeper understanding of the different roles that caregivers assume, as well as their challenges, can contribute to the design and implementation of policies and services that would support their contributions and choices as integral members of the care team.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.030 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".