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Record W2793210102 · doi:10.1177/1403494817743895

Health in All Policies: From rhetoric to implementation and evaluation – the Finnish experience

2018· article· en· W2793210102 on OpenAlexaboutno aff
Timo Ståhl

Bibliographic record

VenueScandinavian Journal of Public Health · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsCharterHealth policyHealth promotionPublic healthPolitical sciencePublic administrationGlobal healthLegislatureDeclarationPublic relationsEconomic growthMedicineNursingEconomics

Abstract

fetched live from OpenAlex

The principles of the Health in All Policies (HiAP) approach are not new. Their international roots can be traced back to 1978 and the Alma-Ata Declaration and the 1986 Ottawa Charter. In Finland, the roots of HiAP go back to 1972 when the Economic Council of Finland, chaired by the Prime Minister, launched the 'Report of the working group exploring the goals of health'. The paper discusses the history, rationale, and implementation of the principles underlying the umbrella concept of HiAP. A rationale for implementing a new concept - HiAP in 2006 during the Finnish European Union presidency - is given. The focus here will be on implementation of HiAP. International material supporting the implementation is introduced and practical examples from Finland presented. The Benchmarking System for Health Promotion Capacity Building is introduced, since it has been used as a primary source of information for monitoring and evaluating HiAP in Finland at the local level. The experience from Finland clearly indicates that HiAP as an approach and as a way of working requires long-term commitment and vision. For working across sectors it is crucial to have data on health and health determinants and analyses of the links between health outcomes, health determinants, and policies across sectors and levels of governance. Intersectoral structures, processes, and tools for the identification of problems and solutions, decisions, and implementation across sectors are prerequisites of HiAP. Legislative backing has proven to be useful, especially in providing continuation and sustainability.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.159
GPT teacher head0.448
Teacher spread0.290 · 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 teacher head, not a consensus.

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

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

Citations63
Published2018
Admission routes1
Has abstractyes

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