Linking Health and Wellbeing in Public Discourse and Policy: The Case of the UK
Bibliographic record
Abstract
Wellbeing has emerged as a defining feature of health policy in recent years. In the UK, a 2014 briefing paper from the Department of Health stated that there is a two-way relationship between health and wellbeing because health has an impact on wellbeing and wellbeing also impacts on health. Good health is a predictor for, or determinant of, a high level of wellbeing, and also an outcome. There is therefore a clear relationship between health and wellbeing. Recent national publications and policy approaches in the UK have incorporated wellbeing within almost all policy prescriptions. This article will consider the complexities of formulating and implementing joint health and wellbeing policies in the UK. It will begin by considering the origins of the health-wellbeing linkage. It will then look at health and wellbeing policy articulation and prescriptions using government policy papers and documents. Finally, it will consider the inherent difficulties of the joint framework approach.
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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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.023 | 0.042 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.018 | 0.012 |
| 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".