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Record W4304775446 · doi:10.3390/nu14204253

The Origins of the Obesity Epidemic in the USA–Lessons for Today

2022· article· en· W4304775446 on OpenAlexaff
Norman J. Temple

Bibliographic record

VenueNutrients · 2022
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsAthabasca University
Fundersnot available
KeywordsObesityEnvironmental healthCalorieConsumption (sociology)Public healthPopularitySugarMedicineDemographyGerontologyFood scienceBiologyPsychology

Abstract

fetched live from OpenAlex

The obesity epidemic appeared in the USA in 1976-1980 and then spread across Westernized countries. This paper examines the most likely causes of the epidemic in the USA. An explanation must be consistent with the emergence of the epidemic in both genders and in all age groups and ethnicities at about the same time, and with a steady rise in the prevalence of obesity until at least 2016. The cause is closely related to changes in the American diet. There is little association with changes in the intake of fat and carbohydrate. This paper presents the opinion that the factor most closely linked to the epidemic is ultra-processed foods (UPFs) (i.e., foods with a high content of calories, salt, sugar, and fat but with very little whole foods). Of particular importance is sugar intake, especially sugar-sweetened beverages (SSBs). There is strong evidence that consumption of SSBs leads to higher energy intake and more weight gain. A similar pattern is also seen with other UPFs. Factors that probably contributed to the increased intake of UPFs include their relatively low price and the increased popularity of fast-food restaurants. Other related topics discussed include: (1) the possible importance of Farm Bills implemented by the US Department of Agriculture; (2) areas where further research is needed; (3) health hazards linked to UPFs; and (4) the need for public health measures to reduce intake of UPFs.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.302

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 head0.337
Teacher spread0.289 · 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 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

Citations78
Published2022
Admission routes1
Has abstractyes

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