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Health economics and nutrition: a review of published evidence

2012· review· en· W2044250237 on OpenAlexaff
Collin Gyles, Irene Lenoir‐Wijnkoop, Jared G. Carlberg, Vijitha Senanayake, Iñaki Gutiérrez‐Ibarluzea, Marten J. Poley, Dominique Dubois, Peter J.H. Jones

Bibliographic record

VenueNutrition Reviews · 2012
Typereview
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPsychological interventionMalnutritionMicronutrientPublic economicsAffect (linguistics)ProductivityEconomic impact analysisEnvironmental healthHealth economicsIntervention (counseling)Economic growthEconomicsMedicinePsychologyHealth care

Abstract

fetched live from OpenAlex

The relationship between nutrition and health-economic outcomes is important at both the individual and the societal level. While personal nutritional choices affect an individual's health condition, thus influencing productivity and economic contribution to society, nutrition interventions carried out by the state also have the potential to affect economic output in significant ways. This review summarizes studies of nutrition interventions in which health-related economic implications of the intervention have been addressed. Results of the search strategy have been categorized into three areas: economic studies of micronutrient deficiencies and malnutrition; economic studies of dietary improvements; and economic studies of functional foods. The findings show that a significant number of studies have calculated the health-economic impacts of nutrition interventions, but approaches and methodologies are sometimes ad hoc in nature and vary widely in quality. Development of an encompassing economic framework to evaluate costs and benefits from such interventions is a potentially fruitful area for future research.

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0080.014
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.129
GPT teacher head0.394
Teacher spread0.265 · 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 designSystematic review
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

Citations51
Published2012
Admission routes1
Has abstractyes

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