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Record W1996700008 · doi:10.1007/s13679-013-0072-9

Obesity, Complexity, and the Role of the Health System

2013· review· en· W1996700008 on OpenAlexafffund
Sarah Frood, Lee M. Johnston, Carrie Matteson, Diane T. Finegood

Bibliographic record

VenueCurrent Obesity Reports · 2013
Typereview
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsSimon Fraser University
FundersSFU Community Trust Endowment FundCanadian Institutes of Health ResearchSimon Fraser University
KeywordsObesityHealthcare systemHealth careSystems thinkingMedicinePsychologyRisk analysis (engineering)Computer sciencePolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

As obesity continues to increase throughout the world, there is still no well-defined solution to the issue. Reducing obesity poses a significant challenge for the health care system because it is a complex problem with numerous interconnections and elements. The complexity of obesity challenges traditional primary care practices that have been structured to address simple or less complicated conditions. Systems thinking provides a way forward for clinicians that are discouraged or overwhelmed by the complexity of obesity. At any given level, individuals matter and system functioning is optimized when our capacity is well matched to the complexity of our tasks. Shifting paradigms around the causes of obesity is essential for creating a health care system that promotes innovative and collaborative practice for healthcare practitioners and individuals dealing with obesity.

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)0.002
metaresearch head score (Gemma)0.004
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: Review
Teacher disagreement score0.009
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.005
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.198
GPT teacher head0.487
Teacher spread0.288 · 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

Citations96
Published2013
Admission routes2
Has abstractyes

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