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Peer Reviewed: A Framework for Addressing the Global Obesity Epidemic Locally: The Child Health Ecological Surveillance System (CHESS)

2008· article· en· W17812923 on OpenAlexaboutno aff
Ronald C. Plotnikoff, Penny Lightfoot, Carla Spínola, Gerry Predy, Linda Barrett

Bibliographic record

VenuePreventing Chronic Disease · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineChildhood obesityAction (physics)Ecological psychologyObesityEnvironmental healthGlobal healthPublic healthEcological systems theoryEcologyNursingOverweightPsychology

Abstract

fetched live from OpenAlex

Childhood obesity has reached epidemic levels in the developed world. Recent research and commentary suggest that an ecological approach is required to address childhood obesity, given the multidimensional nature of the problem. We propose a Canadian prototype, the Child Health Ecological Surveillance System, for a regional health authority to address the growing obesity epidemic. This prototype could potentially be used in other jurisdictions to address other child health issues. We present 8 guiding principles for the development and implementation of a regional framework for action.

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)0.144
metaresearch head score (Gemma)0.227
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.151
Threshold uncertainty score0.759

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1440.227
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0160.022
Scholarly communication0.0230.014
Open science0.0070.012
Research integrity0.0100.008
Insufficient payload (model declined to judge)0.0100.003

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 head0.078
GPT teacher head0.356
Teacher spread0.278 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations0
Published2008
Admission routes1
Has abstractyes

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