The DEsire to DIe in Palliative care: Optimization of Management (DEDIPOM) – a study protocol
Bibliographic record
Abstract
BACKGROUND: A desire to die (DD) is frequent in palliative care (PC). However, uncertainty remains as to the appropriate therapeutic response. (Proactive) discussion of DD is not usually part of standard care. To support health practitioners' (HPs) reactions to a patient's DD, a training program has been developed, piloted and evaluated. Within this framework, a first draft of a semi-structured clinical interview schedule with prompts (CISP) has been developed, including recommendations for action to support HPs' self-confidence. The aim of this study is the further development of the CISP to support routine exploration of death and dying distress and proactive addressing of a DD. METHODS: This observatory, prospective health services study comprises a three step study design: 1. Revision of the CISP and consensus finding based on semi-structured interviews with patients and a Delphi process with (inter-)national experts, patient representatives and relatives; 2. Increasing confidence in HPs through a 2 day-training program using the consented CISP; 3. A formative quantitative evaluation of conversations between HPs and patients (300 palliative patients at three time points) and a qualitative evaluation based on interview triads of patients, relatives and HPs. The evaluation of conversations will include patient-oriented outcomes, including perceived relationships with HPs and death and dying distress. We will also consider aspects of social inequality and gender. DISCUSSION: The intervention can provide a framework for open discussion of DD and a basis for enhancing a trustful HP-patient relationship in which such difficult topics can be addressed. The benefits of this study will include (a) the creation of the first consented semi-structured approach to identify and address DD and to respond therapeutically, (b) the multi-professional enhancement of confidence in dealing with patients' DD and an intervention that can flexibly be integrated into other training and education programs and (c) an evaluation of effects of this intervention on patients, relatives and HPs, with attention to social inequality and gender. TRIAL REGISTRATION: The study is registered in the German Clinical Trials Register ( DRKS00012988 ; registration date: 27.9.2017) and in the Health Services Research Database ( VfD_DEDIPOM_17_003889 ; registration date: 14.9.2017).
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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 | 0.039 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.052 | 0.011 |
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".