Sitting as a moral practice: Older adults’ accounts from qualitative interviews on sedentary behaviours
Bibliographic record
Abstract
Amidst public health campaigns urging people to sit less as well as being more physically active, this paper investigates how older adults make sense of their sedentary behaviour. Using an accounts framework focusing on how people rationalise their sitting practices, we analysed data from 44 qualitative interviews with older adults. All interviewees had received information about sedentary behaviour and health, visual feedback on their own objectively measured sitting over a week and guidance on sitting less. Participants used accounts to position sitting as a moral practice, distinguishing between 'good' (active/'busy') and 'bad' (passive/'not busy') sitting. This allowed them to align themselves with acceptable (worthwhile) forms of sitting and distance themselves from other people whose sitting they viewed as less worthwhile. However, some participants also described needing to sit more as they got older. The findings suggest that some public health messaging may lead to stigmatisation around sitting. Future sedentary behaviour guidelines and public health campaigns should consider more relatable guidelines that consider the lived realities of ageing, and the individual and social factors that shape them. They should advocate finding a balance between sitting and moving that is appropriate for each person.
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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 | 0.024 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".