Humor and laughter in palliative care: An ethnographic investigation
Bibliographic record
Abstract
OBJECTIVE: Humor and laughter are present in most of human interaction. Interactions in health care settings are no exception. Palliative care practitioners know from experience that humor and laughter are common in palliative care despite the seriousness of the care context. Research establishing the significance of humor in care of the dying is limited. METHODS: Clinical ethnography conducted in a 30-bed inpatient palliative care unit served as the means of exploring the functions of humor in care of the dying. Clinical ethnography is intended for examination of the human experience of illness or of caregiving in an interpersonal context (Kleinman, 1992). The method emphasizes the subjective experience and the realm of communication and interaction for both patients and caregivers. Data were collected through participant observation, informal interviews with patients and families, and semistructured interviews with members of the health care team. RESULTS: Humor and laughter were widespread and important in the research setting. An overall attitude of good humor prevailed. Within that atmosphere, humor served myriad functions. Functions were identified in three overarching themes; building relationships, contending with circumstances, and expressing sensibility. Humor among patients, families, and staff most commonly served to build therapeutic relationships, relieve tension, and protect dignity and a sense of worth. Humor was particularly significant in maintaining collegial relationships, managing stressful situations, and maintaining a sense of perspective. SIGNIFICANCE OF RESULTS: Findings established the significance of humor and laughter as humanizing dimensions of care of the dying and contributes to the volume of research supporting evidence-based practice.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".