Defining dignity in terminally ill cancer patients: A factor‐analytic approach
Bibliographic record
Abstract
The construct of 'dignity' is frequently raised in discussions about quality end of life care for terminal cancer patients, and is invoked by parties on both sides of the euthanasia debate. Lacking in this general debate has been an empirical explication of 'dignity' from the viewpoint of cancer patients themselves. The purpose of the present study was to use factor-analytic and regression methods to analyze dignity data gathered from 213 cancer patients having less than 6 months to live. Patients rated their sense of dignity, and completed measures of symptom distress and psychological well-being. The results showed that although the majority of patients had an intact sense of dignity, there were 99 (46%) patients who reported at least some, or occasional loss of dignity, and 16 (7.5%) patients who indicated that loss of dignity was a significant problem. The exploratory factor analysis yielded six primary factors: (1) Pain; (2) Intimate Dependency; (3) Hopelessness/Depression; (4) Informal Support Network; (5) Formal Support Network; and (6) Quality of Life. Subsequent regression analyses of modifiable factors produced a final two-factor (Hopelessness/Depression and Intimate Dependency) model of statistical significance. These results provide empirical support for the dignity model, and suggest that the provision of end of life care should include methods for treating depression, fostering hope, and facilitating functional independence.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".