Reducing social inequalities in health: public health, community health or health promotion?
Bibliographic record
Abstract
While the Consortium on 'Community Health Promotion' is suggesting a definition of this new concept to qualify health practices, this article questions the relevance of introducing such a concept since no one has yet succeeded in really differentiating the three existing processes: public health, community health, and health promotion. Based on a literature review and an analysis of the range of practices, these three concepts can be distinguished in terms of their processes and their goals. Public health and community health share a common objective, to improve the health of the population. In order to achieve this objective, public health uses a technocratic process whereas community health uses a participatory one. Health promotion, on the other hand, aims to reduce social inequalities in health through an empowerment process. However, this is only a theoretical definition since, in practice, health promotion professionals tend to easily forget this objective. Three arguments should incite health promoters to become the leading voices in the fight against social inequalities in health. The first two arguments are based on the ineffectiveness of the approaches that characterize public health and community health, which focus on the health system and health education, to reduce social inequalities in health. The third argument in favour of health promotion is more political in nature because there is not sufficient evidence of its effectiveness since the work in this area is relatively recent. Those responsible for health promotion must engage in planning to reduce social inequalities in health and must ensure they have the means to assess the effectiveness of any actions taken.
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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 | 0.028 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.052 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".