Critically Examining Diversity In End-Of-Life Family Caregiving: Implications for Equitable Caregiver Support and Canada’s Compassionate Care Benefit
Bibliographic record
Abstract
Introduction: Family (i.e., unpaid) caregiving has long been thought of as a 'woman's issue', which ultimately results not only in gendered, but also financial and health inequities.Because of this, gender-based analyses have been prioritized in caregiving research.However, trends in current feminist scholarship demonstrate that gender intersects with other axes of difference, such as culture, socio-economic status, and geography to create diverse experiences.In this analysis we examine how formal front-line palliative care providers understand the role of such diversities in shaping Canadian family caregivers' experiences of end-of-life care.In doing so we consider the implications of these findings for a social benefit program aimed at supporting family caregivers, namely the Compassionate Care Benefit (CCB).Methods: This analysis contributes to a utilization-focused evaluation of Canada's CCB, a social program that provides job security and limited income assistance to Canadian family caregivers who take a temporary leave from employment to provide care for a dying family member at end-of-life.Fifty semi-structured phone interviews with front-line palliative care providers from across Canada were conducted and thematic diversity analysis of the transcripts ensued.Results: Findings reveal that experiences of caregiving are not homogenous and access to services and supports are not universal across Canada.Five axes of difference were commonly raised by front-line palliative care providers when discussing important differences in family caregivers' experiences: culture, gender, geography, lifecourse stage, and material resources.Our findings reveal inequities with regard to accessing needed caregiver services and resources, including the CCB, based on these axes of difference.Conclusions: We contend that without considering diversity, patterns in vulnerability and inequity are overlooked, and thus continually reinforced in health policy.Based on our findings, we demonstrate that re-framing categorizations of caregivers can expose specific vulnerabilities and inequities while identifying implications for the CCB program as it is currently administered.From a policy perspective, this analysis demonstrates why diversity needs to be acknowledged in policy circles, including in relation to the CCB, and seeks to counteract single dimensional approaches for understanding caregiver needs at end-of-life.Such findings illustrate how diversity analysis can dramatically enhance evaluative health policy research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".