Bibliographic record
Abstract
In this chapter arguments are given to avoid overly-simple views of what causes good and ill health, based on simple associations between proximal risk factors and health status. The author discusses the importance of causal processes to understand why two variables are associated and what this association means; what are the mechanisms of the association, and what other key factors may operate in presumptively causal relationships? It is suggested that an ecological view of health is preferable, with attention to biopsychosocial processes from the micro to the macro levels. Further, it is argued that the role that the physical and social environment plays in determining the public’s health is of particular importance to health promoters. This calls for research models that embrace intra-personal, psychosocial and social/cultural processes. Building on the Ottawa principles for health promotion the chapter introduces a model for a whole community approach to health improvement. The model shows how the multi-level processes in community settings and at different system levels shape health. Further, the model makes it clear that understanding these processes are beyond the interests and expertise of any one health discipline and therefore inter-disciplinary approaches are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.012 | 0.028 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".