Health in All Policies: Perspectives From the Region of the Americas
Bibliographic record
Abstract
Abstract There is a broad consensus that the health of an individual or population is not influenced solely by the efforts of the formal health sector; rather, it is also defined by the conditions of daily life as well as the inputs, intentional or not, of various stakeholders and policies. The recognition that health outcomes and inequity in health extend beyond the health sector across many social and government sectors has led to the emergence of a comprehensive policy perspective known as Health in All Policies (HiAP). Building on earlier concepts and principles outlined in the Alma-Ata Declaration (1978) and the Ottawa Charter for Health Promotion (1986), HiAP is a collaborative approach to public policies across sectors that systematically takes into account the health implications of decisions, seeks synergies, and avoids harmful health impacts in order to improve population health and health equity. Health in All Policies has become particularly relevant in light of the adoption of the 2030 Agenda for Sustainable Development and the 17 Sustainable Development Goals (SDGs), as achieving the goals of the agenda requires policy coherence and collaboration across sectors. Given that local governments are ideally positioned to encourage and galvanize partnerships between a diversity of local stakeholders, the implementation of HiAP at the local level is seen as a powerful approach to advancing health and achieving the SDGs through scaled-up initiatives. As there is no single model for the development and implementation of HiAP, it is critical to examine the different experiences across countries that have garnered success in order to identify best practices. The Region of the Americas has made much progress in advancing the HiAP approach, and as such much can be learned from analyzing implementation at country level thus far. Specific initiatives of the Americas may highlight key examples of local action for HiAP and should be taken into consideration for future implementation. Moving forward, it will be important to consider bottom up approaches that directly address the wider determinants of health and health equity.
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 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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".