Community-dwelling older adults' contextual experiencing of humour
Bibliographic record
Abstract
ABSTRACT The purpose of this exploratory study was to examine the contextual experiencing of humour by community-dwelling older adults. Data for this study consisted of audio-recorded, transcribed interviews with 20 older adults who had participated in a larger study of a number of dimensions associated with the process of ageing. Qualitative coding of the interview content was used to extract salient themes that identified types of humour experienced in different life contexts. The analysis of older adults' narratives about their day-to-day lives yielded four types of experienced humour: affiliative, self-enhancing, self-defeating, and authentic. Within an inter-personal context, expressing and appreciating humour contributed to sustaining positive social connections. The use of authentic humour and being able to laugh at one's self and life's uncontrollable circumstances appeared to support a positive sense of self and was adaptive for coping with the inevitable losses that accompany the ageing process, such as declining health status. The results of this study suggest that humour expression and appreciation may play an important role in managing the ageing process in ways that are adaptive especially in inter-personal contexts. Possibly health-care providers in both community and institutional settings need to be made aware of the benefits for older adults of experiencing humour in different life contexts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".