‘My health is not a job’: a qualitative exploration of personal health management and imperatives of the ‘new public health’
Bibliographic record
Abstract
BACKGROUND: There is an increasing push in Western healthcare for people to 'manage' their health, a key aspect of what has been called the 'new public health'. It has been argued that this 'personal health management' - informal work done to monitor, inform, or influence one's health - may be a burden, with potential to contribute to poor health outcomes. However, there is little research actually examining perceptions of personal health management and the 'burden' of these activities, particularly for generally healthy individuals. METHODS: We conducted exploratory qualitative interviews with 30 generally healthy men and women about their perceptions and experiences of personal health management. Questions focused on health behaviours (e.g., information seeking), as well as feelings about these behaviours and perceptions of the time dedicated to health. Audio-recorded interviews were transcribed and analyzed qualitatively using NVivo 10. Where appropriate, quantitative codes were applied and descriptive statistics are reported alongside qualitative findings. RESULTS: Participants were generally satisfied with the amount of time spent on their health and few perceived personal health management as a burden. Many participants took issue with the concept of 'work' being associated with health and stressed the importance of taking personal responsibility for health. CONCLUSIONS: Our findings suggest that generally healthy people have internalised the notion of the 'new public health' and accepted the imperative of personal health responsibility. On the one hand, this bodes well for healthy individuals; their positive attitude may lead to better health outcomes, and the manageable amount of time spent suggests personal health management is unlikely to cause negative health consequences associated with stress. On the other hand, our findings may indicate that other factors, such as social determinants of health, are ignored in health promotion efforts and that those who cannot manage their own health may fall further behind. Future research should continue to explore the time people spend 'working' for their health, and how they perceive and respond to 'new public health' imperatives.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".