Severe mental illness and palliative care: patient semistructured interviews
Bibliographic record
Abstract
OBJECTIVES: To explore perceptions, experiences and expectations with respect to palliative care of patients with severe mental illness (SMI) and an incurable, life-limiting chronic illness. METHODS: Face-to-face semistructured interviews were conducted with 12 patients (10 of them living in a mental healthcare institution) with severe mental and physical health issues in the Netherlands. A semistructured interview guide was used to elicit perceptions of, experiences with and expectations regarding palliative care. Data were analysed using inductive content analysis. RESULTS: Analysis of the data revealed eight categories: perceptions on health and health issues, coping with illness and symptoms, experiences with and wishes for current healthcare, contact with relatives and coresidents, experiences with end of life of relatives and coresidents, willingness to discuss end of life and death, wishes and expectations regarding one's own end of life and practical aspects relating to matters after death. These categories were clustered into two separate themes: current situation and anticipation of end of life. Interviewees with SMI appeared not accustomed to communicate about end-of-life issues, death and dying due to their life-threatening illness. They tended to discuss only their current situation and, after further exploration of the researcher, the terminal phase of life. They seemed not engaged in their future palliative care planning. CONCLUSIONS: Findings of this study highlight inadequacies in advance care planning for patients with SMI. Results suggest using values, current and near wishes, and needs as a starting point for establishing a gradual discussion concerning goals and preferences for future medical and mental treatment and care.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".