A Portuguese trial using dignity therapy for adults who have a life-threatening disease: Qualitative analysis of generativity documents
Bibliographic record
Abstract
OBJECTIVES: Dignity therapy (DT) is a brief, individualized intervention, which provides terminally ill patients with an opportunity to convey memories, essential disclosures, and prepare a final generativity document. DT addresses psychosocial and existential issues, enhancing a sense of meaning and purpose. Several studies have considered the legacy topics most frequently discussed by patients near the end of life. To date, no Portuguese study has done that analysis. METHOD: We conducted a qualitative analysis of 17 generativity documents derived from a randomized controlled trial (RCT). Inductive content analysis was used to identify emerging themes. RESULTS: From the 39 RCT participants receiving DT, 17 gave consent for their generativity document to undergo qualitative analysis. Nine patients were female; mean age of 65 years, with a range from 46 to 79 years. Seven themes emerged: "Significant people and things"; "Remarkable moments"; "Acknowledgments"; "Reflection on the course of life"; "Personal values"; "Messages left to others"; and "Requests and last wishes". SIGNIFICANCE OF RESULTS: Generativity document analysis provides useful information for patients nearing death, including their remarkable life moments and memories, core values, concerns, and wishes for their loved ones. Being conscious of these dominant themes may allow health providers to support humanized and personalized care to vulnerable patients and their families, enhancing how professionals perceive and respond to personhood within the clinical setting.
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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 | 0.048 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".