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Record W1990807694 · doi:10.3109/17477166.2010.512390

Pediatric weight management programs in Canada: Where, What and How?

2010· article· en· W1990807694 on OpenAlexaffabout
Geoff D.C. Ball, Kathryn A. Ambler, Jean‐Pierre Chanoine

Bibliographic record

VenueInternational Journal of Pediatric Obesity · 2010
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of British ColumbiaUniversity of Alberta
Fundersnot available
KeywordsWeight managementMedicineGeneral partnershipReferralMultidisciplinary approachFamily medicinePublic healthMedical educationObesityNursingWeight loss

Abstract

fetched live from OpenAlex

Our purpose was to conduct a national environmental scan of pediatric weight management programs in Canada. Data were entered by program representatives regarding the history, structure, and function of their weight management programs using an online survey that our team developed in partnership with the Canadian Obesity Network ( www.obesitynetwork.ca ). Of the 18 programs that were identified, all included multidisciplinary teams that take a family-centred, lifestyle/behavioural therapeutic approach; health services were accessed primarily through physician referral. Most programs were launched in the past five years with public funding and enrolled ∼125 clients/year into one-on-one and/or group-based weight management care. Although many participated in research and were affiliated with academic institutions, most did not systematically evaluate their obesity-related programming. Based on these observations, recommendations related to program evaluation, health services delivery, and network collaborations are provided to inform future directions for research and clinical care that have both domestic and international relevance.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.239
Teacher spread0.228 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations38
Published2010
Admission routes2
Has abstractyes

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