Empathic communication in dignity therapy: Feasibility of measurement and descriptive findings
Bibliographic record
Abstract
OBJECTIVE: Dignity therapy (DT) is a guided process conducted by a health professional for reviewing one's life to promote dignity through the illness process. Empathic communication has been shown to be important in clinical interactions but has yet to be examined in the DT interview session. The Empathic Communication Coding System (ECCS) is a validated, reliable coding system used in clinical interactions. The aims of this study were (1) to assess the feasibility of the ECCS in DT sessions and (2) to describe the process of empathic communication during DT sessions. METHODS: = 0.84) on 20% of the transcripts and then independently coded the remaining transcripts. RESULTS: Participants were individuals with cancer between the ages of 55 and 75. We developed the ECCS-DT with four empathic response categories: acknowledgment, reflection, validation, and shared experience. We found that of the 235 idea units, 198 had at least one of the four empathic responses present. Of the total 25 DT sessions, 17 had at least one empathic response present in all idea units. SIGNIFICANCE OF RESULTS: This feasibility study is an essential first step in our larger program of research to understand how empathic communication may play a role in DT outcomes. We aim to replicate findings in a larger sample and also investigate the linkage empathic communication may have in the DT session to positive patient outcomes. These findings, in turn, may lead to further refinement of training for dignity therapists, development of research into empathy as a mediator of outcomes, and generation of new interventions.
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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 | 0.073 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".