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Record W7137372548 · doi:10.5737/2368807636193

Humour helps healing: Application of therapeutic humour in palliative care

2025· article· W7137372548 on OpenAlexvenueno aff
Madrika Mirza Kanjiani, Laila Akber Cassum

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2025
Typearticle
Language
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPalliative careAnxietyCoping (psychology)Therapeutic relationshipNarrativePerceptionHealth care

Abstract

fetched live from OpenAlex

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. Keywords: palliative care, therapeutic humour, humour therapy, healing, emotional wellbeing

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.899
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.419
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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