Effects of Dignity Therapy on individuals with amyotrophic lateral sclerosis: Case studies
Bibliographic record
Abstract
OBJECTIVES: To analyze the effects of Dignity Therapy (DT) on the physical, existential, and psychosocial symptoms of individuals with amyotrophic lateral sclerosis (ALS). METHODS: This is a mixed-methods case study research that used the concurrent triangulation strategy to analyze the effects of DT on 3 individuals with ALS. Data collection included 3 instances of administering validated scales to assess multiple physical symptoms, anxiety, depression, spiritual well-being, and the Patient Dignity Inventory (PDI), followed by the implementation of DT and a semi-structured interview. RESULTS: The scale results indicate that DT led to an improvement in the assessment of physical, social, emotional, spiritual, and existential symptoms according to the score results. It is worth noting that the patient with a recent diagnosis showed higher scores for anxiety and depression after DT. Regarding the PDI, the scores indicate improvements in the sense of dignity in all 3 cases, which aligns with the positive verbal reports after the implementation of DT. SIGNIFICANCE OF RESULTS: This study allowed us to analyze the effects of DT on the physical, existential, and psychosocial symptoms of individuals with ALS, suggesting the potential benefits of this approach for this group of patients. Participants reported positive effects regarding pain and fatigue, could reflect on their life trajectories, and regained their value and meaning.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".