Information needs about palliative care and euthanasia: A survey of patients in different phases of their cancer trajectory
Bibliographic record
Abstract
OBJECTIVE: We assessed information provision and information needs about illness course, treatments, palliative care and euthanasia in cancer patients. METHODS: Cancer patients consulting a university hospital (N=620) filled out a questionnaire. Their cancer related data were collected through the treating oncologist. This study is performed in Belgium, where "palliative care for all" is a patient's right embedded in the law and euthanasia is possible under certain conditions. RESULTS: Around 80% received information about their illness course and treatments. Ten percent received information about palliative care and euthanasia. Most information about palliative care and euthanasia was given when the patient had a life expectancy of less than six months. However, a quarter of those in earlier phases in their illness trajectory, particularly those who experienced high pain, fatigue or nausea requested more information on these topics. CONCLUSION: Many patients want more information about palliative care and euthanasia than what is currently provided, also those in an earlier than terminal phase of their disease. PRACTICE IMPLICATIONS: Healthcare professionals should be more responsive, already from diagnosis, to the information needs about palliative care and possible end-of-life decisions. This should be patient-tailored, as some patients want more and some patients want less information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".