The Desire to Die in Palliative Care: A sequential mixed methods study to develop a semi-structured clinical guideline
Bibliographic record
Abstract
Abstract Objective: Although desire to die (DD) of varying intensity and permanence is frequent in patients with advanced cancer and those receiving palliative care, uncertainty exists concerning appropriate therapeutic responses to it. To support health professionals in dealing with patients´ potential DD, a training program and a semi-structured clinical interview guideline was developed. This study aimed for a revision of and consensus building on the clinical guideline to support proactively addressing DD and routine exploration of death and dying distress. Methods: Within a sequential mixed methods design, we invited 16 palliative patients to participate in semi-structured interviews and 377 (inter-)national experts to attend a two-round Delphi process. Interviews were analyzed using qualitative content analysis and an agreement consensus for the Delphi was determined according to predefined criteria. Results : 11 (69%) patients from different settings participated in face-to-face interviews. As key issues for conversations on DD they pointed out the relationship between professionals and patients, the setting and support from external experts, if required. A set of 149 (40%) experts (132/89% from Germany, 17/11% from 9 other countries) evaluated ten domains of the clinical guideline. There was immediate consensus on nine domains concerning conversation design, suggestions for (self-)reflection, and further recommended action. The one domain in which consensus was not achieved until the second round was “proactively addressing DD”. Conclusions : We have provided the first semi-structured clinical guideline to identify and address DD and to respond therapeutically – based on evidence, patients’ views and consensus among (inter-)national professional experts. Trial registration: German Clinical Trials Register (No. DRKS00012988).
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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 | 0.062 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".