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Health in All Policies: Perspectives From the Region of the Americas

2018· reference-entry· en· W2887689625 on OpenAlexaboutno aff
Kira Fortune, Francisco Becerra, Paulo Marchiori Buss, Orielle Solar, Patrícia Tavares Ribeiro, Gabriela E. Keahon

Bibliographic record

VenueOxford Research Encyclopedia of Global Public Health · 2018
Typereference-entry
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsCharterHealth policyPublic healthSocial determinants of healthHealth promotionPopulation healthCivil societyPolitical scienceHealth equityEquity (law)International healthPopulationEconomic growthDeclarationGlobal healthBusinessPublic relationsHealth careEnvironmental healthMedicineEconomicsPolitics

Abstract

fetched live from OpenAlex

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.136
Threshold uncertainty score0.271

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0080.017
Scholarly communication0.0110.005
Open science0.0020.006
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.144
GPT teacher head0.418
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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