Evaluating the impact of early dignity therapy on quality of life in patients with brain tumors: A pilot study
Bibliographic record
Abstract
OBJECTIVES: Brain tumors are associated with negative changes in sense of self and increased distress early in the illness trajectory. Dignity Therapy (DT) is a brief 2-session therapeutic intervention for patients at end-of-life (EOL) that helps conserve a patient's sense of dignity or self. DT has shown positive results for patients at EOL including increased meaning, improved quality of life (QOL), and reduced distress, with limited research to date on patients early in their illness trajectory (non-EOL). This pre-post design pilot study investigated the benefits and feasibility of DT for 2 groups of patients with incurable brain tumors. METHODS: = 18). Participants completed self-report measures to determine changes in QOL, psychosocial well-being (i.e., spiritual well-being, connection, and posttraumatic growth), and death anxiety, at baseline, 1 week, and 5 weeks post-intervention. RESULTS: The intervention had a high completion rate, with 37 of 39 participants (95%) completing DT. Linear regression models fitted with generalized estimating equations (GEEs) showed within- and between-group significant changes in all domains for both groups, but were particularly beneficial for non-EOL participants. SIGNIFICANCE OF RESULTS: This study demonstrated that DT effectively enhanced psychosocial well-being in patients with brain tumors, including reductions in death anxiety and dignity-related distress. Non-EOL participants benefited most and had higher completion rates, highlighting the intervention's feasibility and the need for further research in earlier stages of terminal illness.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".