The interaction of socioeconomic status with place of death: a qualitative analysis of physician experiences
Bibliographic record
Abstract
BACKGROUND: Home is a preferred place of death for many people; however, access to a home death may not be equitable. The impact of socioeconomic status on one's ability to die at home has been documented, yet there remains little literature exploring mechanisms that contribute to this disparity. By exploring the experiences and insights of physicians who provide end-of-life care in the home, this study aims to identify the factors perceived to influence patients' likelihood of home death and describe the mechanisms by which they interact with socioeconomic status. METHODS: In this exploratory qualitative study, we conducted interviews with 9 physicians who provide home-based care at a specialized palliative care centre. Participants were asked about their experiences caring for patients at the end of life, focusing on factors believed to impact likelihood of home death with an emphasis on socioeconomic status, and opportunities for intervention. We relied on participants' perceptions of SES, rather than objective measures. We used an inductive content analysis to identify and describe factors that physicians perceive to influence a patient's likelihood of dying at home. RESULTS: Factors identified by physicians were organized into three categories: patient characteristics, physical environment and support network. Patient preference for home death was seen as a necessary factor. If this was established, participants suggested that having a strong support network to supplement professional care was critical to achieving home death. Finally, safe and sustainable housing were also felt to improve likelihood of home death. Higher SES was perceived to increase the likelihood of a desired home death by affording access to more resources within each of the categories. This included better health and health care understanding, a higher capacity for advocacy, a more stable home environment, and more caregiver support. CONCLUSIONS: SES was not perceived to be an isolated factor impacting likelihood of home death, but rather a means to address shortfalls in the three identified categories. Identifying the factors that influence ability is the first step in ensuring home death is accessible to all patients who desire it, regardless of socioeconomic status.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".