“Everybody in this community is at risk of dying”: An ethnographic exploration on the potential of integrating a palliative approach to care among workers in inner-city settings
Bibliographic record
Abstract
OBJECTIVE: At the end of life, the need for care increases. Yet, for structurally vulnerable populations (i.e., people experiencing homelessness and poverty, racism, criminalization of illicit drug use, stigma associated with mental health), access to care remains highly inaccessible. Emerging research suggests that enhancing access to palliative care for these populations requires moving care from traditional settings, such as the hospital, into community settings, like shelters and onto the street. Thus, inner-city workers (ICWs) (e.g., housing support and community outreach) have the potential to play pivotal roles in improving access to care by integrating a "palliative approach to care" in their work. METHOD: Drawing upon observational field notes and interview data collected for a larger critical ethnographic study, this secondary thematic analysis examines ICWs' (n = 31) experiences providing care for dying clients and garners their perspectives regarding the constraints and facilitators that exist in successfully integrating a palliative approach to care in their work. RESULTS: Findings reveal three themes: (1) Approaches, awareness, and training; (2) Workplace policies and filling in the gaps; and (3) Grief, bereavement, and access to supports. In brief, ICWs who draw upon harm reduction strategies strongly parallel palliative approaches to care, although more knowledge/training on palliative approaches was desired. In their continuous work with structurally vulnerable clients, ICWs have the opportunity to build trusting relationships, and over time, are able to identify those in need and assist in providing palliative support. However, despite death and dying is an everyday reality of ICWs, many described a lack of formal acknowledgement by employers and workplace support as limitations. SIGNIFICANCE OF RESULTS: Findings contribute promising practices for enhancing equitable access to palliative care for society's most vulnerable populations by prioritizing front-line workers' perspectives on how best to integrate a palliative approach to care where structurally vulnerable populations live and die.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".