Meaning Making as a Central Mechanism of Dignity Therapy for Older Adults With Cancer
Bibliographic record
Abstract
Abstract Nearly 500,000 older Americans die a cancer-related death each year (National Vital Statistics Report, 2018). Following a diagnosis of a serious illness like cancer, maintaining a sense of dignity is central to a patient’s wellbeing. Dignity Therapy (DT) was recently introduced as an intervention to enhance dignity for terminally ill patients (Chochinov et al., 2005). This therapy provides patients opportunities to foster a sense of dignity though making meaning of their lives (Hack et al., 2010). To date, whether meaning-making actually occurs as a central mechanism of effective DT has not been tested. The current study investigates (i) how often and in what forms meaning-making occurs during DT, and (ii) how patients’ baseline feelings of dignity relate to meaning-making during DT. Participants were 25 male and female cancer outpatients (M age = 63.08; SD = 5.72). They completed the Patient Dignity Inventory (Chochinov et al., 2008) and then participated in Dignity Therapy with a trained provider. Sessions were audio recorded, transcribed, and reliably content-analyzed for meaning-making using an established coding scheme (Park & Folkman, 1997). Content-analysis revealed that all patients made meaning of past life events at least once (range: 1-12 occurrences). Multiple forms of meaning-making emerged, with Finding Benefit and Personal Growth most common. Patients reporting more dignity-related distress prior to DT showed greater meaning-making during the DT session (r = .46, p < 0.05). This study provides foundational evidence that meaning-making is a key mechanism of Dignity Therapy, helping older adults with cancer enhance dignity at end-of-life.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".