MétaCan
Menu
Back to cohort
Record W2784495931 · doi:10.1111/obr.12639

Childhood obesity policies – mighty concerns, meek reactions

2017· article· en· W2784495931 on OpenAlexaboutno aff
Signild Vallgårda

Bibliographic record

VenueObesity Reviews · 2017
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsChildhood obesityObesityPolitical scienceMedicineEnvironmental healthOverweightInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The increasing number of children defined as overweight or obese is causing concern among politicians and health advocates; several countries have launched policies addressing the issue. METHOD: The paper presents an analysis of how the childhood obesity is defined, explained and suggested policies to address the problem from the WHO, the EU, Canada, England and New Zealand. RESULTS: Considering the dramatic language used when describing childhood obesity, the proposed interventions are modest. Either the politicians do not consider the problem that great after all, or other concerns, such as the freedom of the food and drink industry and local authorities, are seen as more important. The causes identified are multiple and varied, including the physical and commercial environment, whereas the interventions primarily address the information level of the population, placing responsibility on the shoulders of the parents. Only the World Health Organization argues that statutory measures are required, and the English Government suggests one: a levy on sugary drinks. Otherwise, local authorities, schools and the industry are expected to act on a voluntary basis. Very little is explicitly substantiated by evidence, and the evidence cited is sometimes misinterpreted or disregarded. CONCLUSION: There is a discrepancy between how the problem of childhood obesity is presented as alarming and the modest measures suggested.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.052
GPT teacher head0.337
Teacher spread0.285 · 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.

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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueObesity ReviewsSame topicObesity, Physical Activity, DietFrench-language works237,207