Promoting hope at the end of life: effectiveness of an intervention programme
Bibliographic record
Abstract
Background: Hope is essential to life and a coping strategy for people with advanced chronic illness facing the end of life. Objectives: Test the effect of an intervention Programme to Promote Hope (PPH) on hope, quality of life (QOL) and comfort of people in palliative situation. Methods: Fifty-six (56) palliative patients were randomly assigned to an intervention group (n = 28) and a control group (n = 28). The intervention group was provided with 3 PPE sessions (1 hour) for one week, while the control group were submitted to the regular treatment. All patients were assessed with the Portuguese versions of the Herth Hope Index, McGill Quality of Life Questionnaire and Holistic Comfort Questionnaire at pre-test/post-test, at one month follow up. The dropouts resulted in n = 11 in intervention group and 12 in control group. Results: Patients experienced good levels of hope, QOL and comfort. There was no significant difference at the pre-test of outcome measures (p > 0.05). After the intervention significant differences existed between the 2 groups in Hope and QOL, but no differences in comfort levels. Follow-up hope levels decreased with significant differences between the groups (p < 0.05). Differences in QOL were not found. Comfort levels were significantly different in follow up, with higher levels of comfort experienced by the intervention group. Conclusions: A structural hope intervention programme in palliative patients in a community setting had a positive effect on improving hope, quality of life and comfort. More studies need to be developed to test the intervention on a bigger sample.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".