The well‐being economy and health in all policies: Fostering action for change
Bibliographic record
Abstract
This special issue, the 'Well-being Economy and Health in All Policies: Fostering Action', highlights the need for societal change in the way we think about health and well-being.It brings two separate but related concepts together-the well-being economy and Health in All Policies (HiAP).The first concept seeks changes to the way society values and measures progress; and the second requires decisionmakers to work across departmental boundaries to use public policy to create the conditions for health and well-being.The papers included in the special issue explore these two concepts from different perspectives.They unpack the connections between the wellbeing economy and HiAP, share some examples of promising practice, and provide insights into the challenges and opportunities that need to be acted on if we are to move towards a flourishing well-being society; where decision-makers work collaboratively to care for people, planet and future generations.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.026 | 0.024 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.020 | 0.026 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".