The meaning of'health improvement'
Bibliographic record
Abstract
Objective To explore how those working in and with primary care organisations understand the term 'health improvement'. Design Six qualitative case studies. Setting Primary Care Groups and Trusts and partner organisations. Method Semi-structured interviews with senior personnel from participating organisations. Results Informants offered a wide range of definitions which generally combined more than one meaning. Replies ranged from, on the one hand, an emphasis on National Health Service (NHS) service provision to, on the other, an emphasis on the socioeconomic determinants of population health, or the quality of life of individuals. Some explained the term primarily as a government strategy; some, as a set of activities for the NHS; some, in terms of the overarching purpose of health improvement. Conclusion Our informants offered more detailed definitions of 'health improvement' than are articulated in Department of Health documents. However, they did not include health inequalities in their definition. Recent government announcements about health inequalities targets may help to ensure that all primary care organisations consider the inequalities in the health of their population.
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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 | 0.040 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.081 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".