Bibliographic record
Abstract
Humor and laughter are ubiquitous in human interactions. Terminal illness, however, is often accompanied by circumstances of anxiety, fear, and sadness. Hospice/palliative care emphasizes quality of life and the importance of human relationships. In this context, humor finds its place in authentic person-to-person connectedness. This article presents findings from a clinical ethnography that investigated the phenomena of humor and laughter in an inpatient palliative care unit. As a participant observer, the lead author accompanied 6 nurses throughout their day-to-day activities, twice weekly over 12 weeks. In addition to more than 200 hours of fieldwork, informal conversations were held with patients and families and semistructured interviews were conducted with nurses (n = 11), physicians (n = 2), a social worker (n = 1), and a physiotherapist (n = 1). Humor was pervasive, varied in the setting, and occurred across a range of intensities. Both clients and team members used humor to build relationships, contend with circumstances, and express sensibilities. Humor was affected by differences in people, differing circumstances, ethnicity, gender, and degree of stress. Participants relied on intuition as well as a constellation of other factors in discerning whether or not to use humor. Techniques for assessment included identification of cues such as expression in the eyes and timing as indications of receptivity. Combined with caring and sensitivity, humor is a powerful therapeutic asset in hospice/palliative care. It must neither be taken for granted nor considered trivial.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.150 | 0.062 |
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".