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Record W2110747157 · doi:10.1586/17446651.2014.897605

<i>It’s like rocket science…only more complex</i>: challenges and experiences related to managing pediatric obesity in Canada

2014· article· en· W2110747157 on OpenAlexaffabout
Jillian L.S. Avis, Tracey Bridger, Annick Buchholz, Jean‐Pierre Chanoine, Stasia Hadjiyannakis, Jill Hamilton, Mary Jetha, Laurent Legault, Katherine M. Morrison, Anne Wareham, Geoff D.C. Ball

Bibliographic record

VenueExpert Review of Endocrinology & Metabolism · 2014
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsMcMaster UniversityMcGill UniversitySickKids FoundationAlberta Health ServicesStollery Children's HospitalHospital for Sick ChildrenUniversity of British ColumbiaChildren's Hospital of Eastern OntarioJaneway Children's Health and Rehabilitation CentreMemorial University of NewfoundlandUniversity of TorontoBC Children's HospitalUniversity of Alberta
Fundersnot available
KeywordsOverweightObesityMedicinePublic healthChildhood obesityHealth careMedical educationFamily medicineGerontologyNursingPolitical science

Abstract

fetched live from OpenAlex

Pediatric obesity is an urgent and complex public health issue. Approximately one-third of Canadian children are overweight or obese, a proportion that highlights the need for effective and accessible services to improve short- and long-term health risks. In our experience, we have encountered a number of challenges common in pediatric obesity management across our clinical and research centers. For the purpose of this review, these challenges and our real-world experiences are grouped as issues that span (i) caring for children, adolescents, and families, (ii) collaborating with colleagues and (iii) working within the health care system. Collectively, we highlight a number of lessons learned from our years of experience and detail ongoing initiatives designed to optimize health services for managing obesity for children and adolescents in Canada.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.306
Teacher spread0.286 · 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.

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

Citations29
Published2014
Admission routes2
Has abstractyes

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