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Record W4388583556 · doi:10.5888/pcd20.230159

Suboptimal Intake of Fruits and Vegetables in Nine Selected Countries of the World Health Organization European Region

2023· article· en· W4388583556 on OpenAlexaff
Holly Rippin, Katerina Maximova, Enrique Loyola, João Breda, Kremlin Wickramasinghe, Carina Ferreira‐Borges, Nino Berdzuli, Morteza Hajihosseini, Irina Novik, Vital Pisaryk, Lela Sturua, Ainura Akmatova, Galina Obreja, Saodat Azimzoda Mustafo, Banu Ekinci, Toker Ergüder, Shukhrat Shukurov, Gahraman Hagverdiyev, Diana Andreasyan, Sergei Bychkov, Ivo Rakovac

Bibliographic record

VenuePreventing Chronic Disease · 2023
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsPublic Health OntarioUniversity of TorontoUniversity of AlbertaSt. Michael's Hospital
FundersWorld Health Organization
KeywordsMedicinePublic healthHealth promotionDiseaseChronic diseaseEnvironmental healthPromotion (chess)Peer reviewFamily medicineGerontologyNursingPathology

Abstract

fetched live from OpenAlex

What is already known on this topic?Low rates of fruit and vegetable intake are associated with increased risk of noncommunicable diseases (NCDs).Although the disease burden due to inadequate fruit and vegetable consumption appears highest in Eastern Europe and Central Asia among countries in the World Health Organization (WHO) European Region, little systematic evidence exists.What is added by this report?Higher NCD death rates in Eastern Europe and Central Asia may be partly explained by differences in diet quality, particularly low rates of fruit and vegetable intake.Most populations in our study did not meet the WHOrecommended daily intake of at least 5 servings (400 g).What are the implications for public health practice?Evidence-based policies are needed to increase fruit and vegetable consumption and reduce the burden of and disparities in NCDs.Our findings can inform further research and policy development.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.014
GPT teacher head0.268
Teacher spread0.254 · 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 designObservational
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

Citations18
Published2023
Admission routes1
Has abstractyes

Explore more

Same venuePreventing Chronic Disease→Same topicNutritional Studies and Diet→French-language works237,207→