Describing the characteristics and symptom profile of a group of urban patients experiencing socioeconomic inequity and receiving palliative care: a descriptive exploratory analysis
Bibliographic record
Abstract
Background: Individuals experiencing socioeconomic inequity have worse health outcomes and face barriers to palliative and end-of-life care. There is a need to develop palliative care programs tailored to this underserved population. Objectives: To understand the characteristics and symptom profiles of a group of urban patients experiencing socioeconomic inequity and receiving palliative care. Design: Descriptive exploratory analysis of a patient dataset. The patient dataset was generated through a pilot research study with patients experiencing socioeconomic inequity and life-limiting illness who received a community-based palliative care intervention. Methods: The intervention took place over 1 year in the Palliative Care Outreach and Advocacy Team, a community-based urban palliative care clinic in Edmonton, Alberta, Canada, serving persons experiencing socioeconomic inequity. Participants had to be at least 18 years of age, be able to communicate in English, require palliative care for a life-limiting illness, and be able to consent to inclusion in the study. Results: Twenty-five participants were enrolled. Participants predominantly identified as male and Indigenous, experienced poverty and housing instability, and had metastatic cancer. Our participants rated their pain, shortness of breath, and anxiety as more severe than the broader community-based palliative care population in the same city. Most patients died in inpatient hospices (73%). Conclusion: Our analysis provides an in-depth picture of an understudied, underserved population requiring palliative care. Given the higher symptom severity experienced by participants, our analysis highlights the importance of person-centered palliative care. We suggest that socioeconomic inequity should be considered in patients with life-limiting illnesses. Further research is needed to explore palliative care delivery to those facing socioeconomic inequity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".