Happy and healthy only if occupied? Perceptions of health sciences students on occupation in later life
Bibliographic record
Abstract
BACKGROUND/AIM: In this study, we bring attention to the university education of health science students with respect to occupation in later life. Our goal was to provide descriptive data from narratives of a group of undergraduate students and initiate discussion about the place of occupation in the context of ageing to answer the following questions: (i) How young people perceive successful ageing in relation to occupation? and (ii) can spirituality-related activities be considered occupations in later life? METHODS: Based on a thematic selection, the quality of photographs and reflective narratives, 60 Photovoice assignments created by health sciences students were analysed using content analysis. RESULTS: The findings of this study indicate that students seem to neglect the benefits of 'being' through spiritual engagement, and instead emphasise the importance of 'doing', and perpetuate pervasive successful ageing discourses in Western societies. CONCLUSIONS: Occupational therapists have potential to take an active role in undergraduate health science education and to inform the development of holistic models that would include spirituality as an avenue to live late life to its fullest potential. Photovoice emerged as a powerful teaching method to increase awareness, empathy and compassion of young adults towards ageing.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".