Dignity therapy in Mexican lung cancer patients with emotional distress: Impact on psychological symptoms and quality of life
Bibliographic record
Abstract
Lung cancer (LC) is the most frequent and deadly neoplasm in the world, and patients have shown a tendency to have more emotional distress than other cancer populations. Dignity Therapy (DT) is a brief intervention aimed to improve emotional well-being in patients facing life-threatening illness. OBJECTIVE: To analyze the effect of DT on anxiety, depression, hopelessness, emotional distress, dignity-related distress, and quality of life (QoL) in a group of Mexican patients with stage IV LC undergoing active medical treatment with baseline emotional distress. METHOD: In this preliminary pretest-posttest study, patients received three sessions of DT and were evaluated with the HADS, Distress Thermometer, Patient Dignity Inventory, single-item questions, and QLQ-30. RESULTS: In total, 24 out of 29 patients completed the intervention. Statistically significant improvements were found in anxiety, depression, emotional distress, hopelessness, and dignity-related distress with large effect sizes. Patients reported that DT helped them, increased their meaning and purpose in life, their sense of dignity, and their will to live, while it decreased their suffering. No changes were found in QoL. SIGNIFICANCE OF RESULTS: DT was well accepted and effective in improving the emotional symptoms of LC patients with distress that were undergoing medical treatment. Although more research is warranted to confirm these results, this suggests that DT can be used in the context of Latin-American patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".