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Record W3081208675 · doi:10.1007/s00394-020-02359-1

Measuring health promotion: translating science into policy

2020· article· en· W3081208675 on OpenAlexaff
James C. Griffiths, Jan de Vries, Michael I. McBurney, Suzan Wopereis, Samet Serttas, Daniel S. Marsman

Bibliographic record

VenueEuropean Journal of Nutrition · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsUniversity of Guelph
FundersWorld Health Organization
KeywordsHealth promotionQuality of life (healthcare)Psychological resilienceFlexibility (engineering)CLARITYMedicineHealth literacyGerontologyPromotion (chess)Health educationMental healthHealth policyPsychologyEnvironmental healthHealth carePublic healthNursingPolitical scienceSocial psychologyPsychiatryBiology

Abstract

fetched live from OpenAlex

Commonly, it is the end of life when our health is deteriorating, that many will make drastic lifestyle changes to improve their quality of life. However, it is increasingly recognized that bringing good health-promoting behaviors into practice as early in life as possible has the most significant impact across the maximal healthspan. The WHO has brought clarity to health promotion over the last fifteen years, always centering on language relating to a process of enabling people to increase control over, and to improve, their physical, mental and social health. A good healthspan is not just freedom from morbidity and mortality, it is that joie de vivre ("joy of living") that should accompany every day of our lifespan. Therefore, health promotion includes not only the health sector, but also needs individual commitment to achieve that target of a healthspan aligned with the lifespan. This paper explores health promotion and health literacy, and how to design appropriate nutritional studies to characterize contributors to a positive health outcome, the role the human microbiome plays in promoting health and addressing and alleviating morbidity and diseases, and finally how to characterize phenotypic flexibility and a physiologic resilience that we must maintain as our structural and functional systems are bombarded with the insults and perturbations of life.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.048
GPT teacher head0.285
Teacher spread0.237 · 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 designBench or experimental
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

Citations15
Published2020
Admission routes1
Has abstractyes

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