Humour helps healing: Application of therapeutic humour in palliative care
Bibliographic record
Abstract
This narrative review aims to identify the available scientific literature related to the application of therapeutic humour in palliative care. The databases Google Scholar, PubMed, and Science Direct, were searched using relevant keywords, such as "therapeutic humor in palliative care", "therapeutic humor AND palliative care", and "therapeutic humor AND end-of-life care". In total, 14 articles published between 2014 and 2024 were selected. The findings highlight various perspectives on the application of therapeutic humour in palliative care, emphasizing its role in enhancing coping skills, reducing pain and anxiety, improving mood, and strengthening nurse-patient and nurse-family relationships. Additionally, the acceptability of therapeutic humour among palliative care patients, the impact of humour therapy on healthcare providers, and the appropriate timing and integration of humour into patient care are also explored. Therapeutic humour can be an effective complementary therapy in palliative care, providing maximum comfort to patients and reducing family anxiety by reducing pain perception and alleviating stress. Therapeutic humour has been found to be beneficial for palliative care patients, but adequate training of healthcare providers is required to incorporate it effectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".