The end of life seen through the eyes of the patient – a qualitative study through individual in-depth interview
Bibliographic record
Abstract
Introduction: Conversations about dying have always been a challenge for clinicians and people with long-term disease.The purpose of this article is to explore the patients' perspective on death and dying in the context of their condition and illness and to examine well-being of patients before and after interviews. Material and methods:Triangulation of methods was used in the study.A qualitative strategy used an individual in-depth interview method, following the author's interview script to identify patients' perceptions of their own end of life.A quantitative strategy was used to examine disease-related symptoms using the Edmonton symptom assessment system (ESAS) scale (pre-and post-interview test).The subjects were 15 patients in the advanced stages of cancer from the Hospice in Bydgoszcz.Qualitative data were analyzed and interpreted according to the principles of content analysis.Results: Factors triggering thoughts of death primarily included state of health, ailments or loneliness.Patients associate death with anxiety and the very moment of dying.However, some of the respondents did not present fear of impending death.As regards death, patients are mainly afraid of the very moment of dying, accompanied by suffering and loneliness.Believers emphasize the role of spiritual needs.Most often patients wanted to die at home.They usually wished for the presence of a loved one and at the same time, expressed concern and care for them.According to the ESAS scale, symptoms decreased almost in all assessed ranges.Conclusions: Patients' readiness to talk about death and dying is varied, but is helpful in reducing symptoms.An important factor is the degree of willingness of both parties to address this difficult issue.Talking about the end of life is helpful in relieving symptoms and reducing anxiety.Increasing well-being during and after a conversation on existential and spiritual issues invites conversations on these topics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".