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Record W1605012663 · doi:10.1159/000368136

Beyond Weight Loss: Experiences and Insights Related to Working Effectively with Families and Operating within the Health Care System to Manage Pediatric Obesity

2015· book-chapter· en· W1605012663 on OpenAlexaff
Geoff D.C. Ball, Jillian L.S. Avis, Annick Buchholz, Tracey Bridger, Jean‐Pierre Chanoine, Stasia Hadjiyannakis, Jill Hamilton, Laurent Legault, Katherine M. Morrison, Anne Wareham, Mary Jetha

Bibliographic record

VenuePediatric and adolescent medicine · 2015
Typebook-chapter
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsMcMaster UniversityMcMaster Children's HospitalUniversity of TorontoMontreal Children's HospitalSickKids FoundationBC Children's HospitalHospital for Sick ChildrenUniversity of British ColumbiaJaneway Children's Health and Rehabilitation CentreMemorial University of NewfoundlandMcGill UniversityChildren's Hospital of Eastern OntarioUniversity of Alberta
Fundersnot available
KeywordsObesityOverweightPublic healthHealth careMedicineNursingEnvironmental healthGerontologyPolitical science

Abstract

fetched live from OpenAlex

Pediatric obesity is an urgent and complex public health issue. The high proportions of children who meet the definition of overweight or obesity highlight the need for effective and accessible services to reduce 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 include (i) caring for children with obesity and their families and (ii) working within the health care system. Overall, we highlight a number of lessons learned from our years of experience and detail ongoing initiatives designed to optimize health services for managing obesity developed for children and their families.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.029
GPT teacher head0.337
Teacher spread0.307 · 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 designQualitative
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

Citations1
Published2015
Admission routes1
Has abstractyes

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