“Just too busy living in the moment and surviving”: barriers to accessing health care for structurally vulnerable populations at end-of-life
Bibliographic record
Abstract
BACKGROUND: Despite access to quality care at the end-of-life (EOL) being considered a human right, it is not equitable, with many facing significant barriers. Most research examines access to EOL care for homogenous 'normative' populations, and as a result, the experiences of those with differing social positioning remain unheard. For example, populations experiencing structural vulnerability, who are situated along the lower rungs of social hierarchies of power (e.g., poor, homeless) will have unique EOL care needs and face unique barriers when accessing care. However, little research examines these barriers for people experiencing life-limiting illnesses and structural vulnerabilities. The purpose of this study was to identify barriers to accessing care among structurally vulnerable people at EOL. METHODS: Ethnography informed by the critical theoretical perspectives of equity and social justice was employed. This research drew on 30 months of ethnographic data collection (i.e., observations, interviews) with structurally vulnerable people, their support persons, and service providers. Three hundred hours of observation were conducted in homes, shelters, transitional housing units, community-based service centres, on the street, and at health care appointments. The constant comparative method was used with data collection and analysis occurring concurrently. RESULTS: Five significant barriers to accessing care at EOL were identified, namely: (1) The survival imperative; (2) The normalization of dying; (3) The problem of identification; (4) Professional risk and safety management; and (5) The cracks of a 'silo-ed' care system. Together, findings unveil inequities in accessing care at EOL and emphasize how those who do not fit the 'normative' palliative-patient population type, for whom palliative care programs and policies are currently built, face significant access barriers. CONCLUSIONS: Findings contribute a nuanced understanding of the needs of and barriers experienced by those who are both structurally vulnerable and facing a life-limiting illness. Such insights make visible gaps in service provision and provide information for service providers, and policy decision-makers alike, on ways to enhance the equitable provision of EOL care for all populations.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".