Bibliographic record
Abstract
BACKGROUND AND AIM: To inform health promotion interventions, there is a need for large studies focusing specifically on what makes older adults feel good, from their own perspective. The aim was to explore older adults' views of what makes them feel good in relation to their different characteristics. METHODS: A qualitative and quantitative study design was used. Independently living people (n = 1212, mean age 78.85) answered the open-ended question, 'What makes you feel good?' during preventive home visits. Following inductive and summative content analysis, data was deductively sorted, based on The Canadian model of occupational performance and engagement, into the categories leisure, productivity, and self-care. Group comparisons were made between: men/women; having a partner/being single; and those with bad/good subjective health. RESULTS: In total, 3117 notes were reported about what makes older adults feel good. Leisure activities were the most frequently reported (2501 times), for example social participation, physical activities, and cultural activities. Thereafter, productivity activities (565 times) such as gardening activities and activities in relation to one's home were most frequently reported. Activities relating to self-care (51 times) were seldom reported. There were significant differences between men and women, having a partner and being single, and those in bad and good health, as regards the activities they reported as making them feel good. DISCUSSION AND CONCLUSIONS: To enable older adults to feel good, health promotion interventions can create opportunities for social participation and physical activities which suit older adults' needs. Such interventions should be adapted to different groups.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".