Exploring unmet concerns in home hospice cancer care: Perspectives of patients, informal caregivers, palliative care providers, and family physicians
Bibliographic record
Abstract
Abstract Objectives The study examines perspectives of patients in home hospice care; their informal caregivers; palliative health-care providers (HCPs); and family physicians, all regarding patients’ unmet needs and quality of life (QoL)-related concerns. Methods Participants from all 4 groups were approached within 2 months after the patient’s admission to the home hospice care unit. Participants completed Edmonton Symptom Assessment Scale (ESAS) and Measure Yourself Concerns and Wellbeing (MYCAW) questionnaires, for patient’s QoL-related concerns. Qualitative analysis of short narratives was conducted using ATLAS.ti software for systematic coding. Results In total, 78 participants completed the study questionnaires: 24 patients, 22 informal caregivers, 22 palliative HCPs, and 11 family physicians. Informal caregivers gave higher scores (i.e., greater severity) than patients for fatigue on ESAS (p = 0.009); and family physicians lower scores than patients for ESAS drowsiness (p = 0.046). Compared with patients, palliative HCPs gave higher scores for patient emotional-spiritual concerns (77.2% vs. 41.7%, p = 0.02); lower scores for gastrointestinal concerns (p = 0.048); and higher scores for overall function (p = 0.049). Qualitative assessment identified a gap between how patients/informal caregivers vs. palliative HCPs/family physicians regard emotional-spiritual themes, including discussing issues related to death and dying. Significance of results The findings of the present study suggest that exploring a multifaceted cohort of home hospice patients, informal caregivers, palliative HCPs, and family physicians may provide insight on how to reduce communication gaps and address unmet needs of patients, particularly regarding emotional and spiritual concerns. Conclusions While the 4 groups were similar in their scoring of patient QoL-related concerns, there were discrepancies for some concerns (e.g., patient fatigue) and expectations regarding the need to discuss emotional and spiritual concerns, including on death and dying. Educational initiatives with programs providing training to all 4 groups may help bridge this gap, creating a more open and collaborative hospice care environment.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".