Reducing social inequalities in health: public health, community health or health promotion?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.073 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it