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Record W2150447335 · doi:10.2105/ajph.2013.301486

Obesity, Health at Every Size, and Public Health Policy

2013· article· en· W2150447335 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAmerican Journal of Public Health · 2013
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsDietingObesityPublic healthEnvironmental healthMedicineGerontologyWeight lossAffect (linguistics)Health careHealth policyHealth promotionDiseasePsychologyPolitical scienceEndocrinologyNursing

Abstract

fetched live from OpenAlex

Obesity is associated with chronic diseases that may negatively affect individuals' health and the sustainability of the health care system. Despite increasing emphasis on obesity as a major health care issue, little progress has been made in its treatment or prevention. Individual approaches to obesity treatment, largely composed of weight-loss dieting, have not proven effective. Little direct evidence supports the notion of reforms to the "obesogenic environment." Both these individualistic and environmental approaches to obesity have important limitations and ethical implications. The low levels of success associated with these approaches may necessitate a new non-weight-centric public health strategy. Evidence is accumulating that a weight-neutral, nutrition- and physical activity-based, Health at Every Size (HAES) approach may be a promising chronic disease-prevention strategy.

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.

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)0.021
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.511
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.107
GPT teacher head0.465
Teacher spread0.359 · 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