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Record W4382403502 · doi:10.3390/nu15132910

A Proposed Strategy against Obesity: How Government Policy Can Counter the Obesogenic Environment

2023· review· en· W4382403502 on OpenAlexaff
Norman J. Temple

Bibliographic record

VenueNutrients · 2023
Typereview
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsAthabasca University
Fundersnot available
KeywordsObesityHealthy foodGovernment (linguistics)Promotion (chess)Psychological interventionBusinessHealth promotionEnvironmental healthPopulationHealthy dietSubsidyMarketingMedicinePublic healthPolitical scienceFood sciencePoliticsBiology

Abstract

fetched live from OpenAlex

An epidemic of obesity emerged in the USA in 1976-1980. The epidemic then spread to many other Westernized nations. Many interventions have been carried out with the goal of lowering the prevalence of obesity. These have mostly taken the form of various types of health promotion (i.e., providing people with education, advice, and encouragement). These actions have achieved, at most, only limited success. A strategy with a better chance of success starts with the recognition that the fundamental cause of obesity is that we live in an obesogenic environment. It is therefore necessary to change the environment so that it fosters a generally healthy lifestyle, thereby leading to enhanced health for the population, including improved weight control. A major goal is to increase the intake of healthy foods (especially fruit, vegetables, and whole grains), while decreasing intake of unhealthy foods (especially ultra-processed foods such as sugar). This will require major changes of many government policies. Some of the required policies are as follows. Schools should implement policies that create a healthy environment for children. For example, they should adopt a policy that only foods of high nutritional quality are sold in vending machines or given to students within school meals. Policies need to go well beyond the school setting; a broad strategy is needed that creates a healthy environment for children. Another important policy is the manipulation of food prices in order to shift the diet toward healthy foods. This requires using subsidies to lower the price of healthy foods, while adding a tax to less healthy foods to increase the price. This policy has been implemented in many cities and countries in the form of a tax on sugar-sweetened beverages (SSBs). The advertising of unhealthy foods (including fast-food restaurants) should be banned, especially where children and adolescents are the major target. Such a ban could be extended to a complete ban on all advertising for unhealthy foods, including that directed at adults. The proposed policy measures are likely to be strongly opposed by food corporations.

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)0.016
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.028
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0050.013
Scholarly communication0.0170.015
Open science0.0030.007
Research integrity0.0230.015
Insufficient payload (model declined to judge)0.0130.003

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 head0.046
GPT teacher head0.305
Teacher spread0.259 · 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 designNot applicable
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

Citations38
Published2023
Admission routes1
Has abstractyes

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