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Record W2322835031 · doi:10.1158/1940-6207.prev-12-b74

Abstract B74: Raising healthy youth: Using the Manitoba Youth Health Survey to identify predictive factors of childhood obesity

2012· article· en· W2322835031 on OpenAlexaffabout
Jane Griffith, Tannis Erickson, Katherine Fradette, Oliver Bucher, Carly Leggett, Kate McGarry, Beth Harland

Bibliographic record

VenueCancer Prevention Research · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsCancerCare Manitoba
Fundersnot available
KeywordsPublic healthAgency (philosophy)Active livingCurriculumGerontologyEnvironmental healthChildhood obesityHealth educationMedicineObesityPolitical scienceSociologyNursingOverweight

Abstract

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Abstract In 2006, schools, communities and regional health authorities (RHAs) in Manitoba, Canada recognized the need for local risk factor surveillance data, and formed Partners in Planning for Healthy Living (PPHL) to pool in kind human resources, financial support and expertise in the pursuit of this common goal. PPHL was established as a network of partners involving collaboration with all Manitoba's RHAs, Manitoba Education, Manitoba Healthy Living (Healthy Schools program), Healthy Child Manitoba, Manitoba Health, NGOs, Manitoba Physical Education Supervisors' Association and the Public Health Agency of Canada Manitoba/Saskatchewan division. Between 2005 and 2008, PPHL implemented the Youth Health Survey (YHS) to explore the physical activity, healthy eating, BMI, tobacco use, substance use and school connectedness of Manitoba's youth. The survey was administered to students in grades 6-12 at more than 400 schools across Manitoba including First Nations, francophone and independent schools (n= 48,449). Local-level reports were provided to schools, school divisions and RHAs. The YHS data has been used extensively by schools, regional health authorities and other community health partners to help in planning and implementing healthy school policies and programs with support from Manitoba Healthy Living, RHAs and other community partners. The data has also provided a baseline for the evaluation of the recently implemented Grades 11 and 12 Active Healthy Lifestyles: Physical Education/Health Education curriculum. Secondary analysis of the data has recently begun to delve deeper into the relationships among the variables. One of the key areas of analysis is childhood obesity and its related factors. Obesity and its causes are major contributing factors to the development of certain cancers. Childhood obesity rates are rising at an alarming pace and as such, the development of programs aimed at curbing this rise is of primary importance to PPHL and its members. Recent research has provided information on the lifelong risks of childhood obesity which leads to healthy eating and adequate physical activity being top priorities in Manitoba. The purpose of our study is to identify factors related to and contributing to obesity levels of the children and youth who participated in the Manitoba YHS. A multilevel logistic regression approach with students nested within schools will be used to investigate both student- and school-level factors associated with (1) being underweight vs. a healthy weight and (2) being overweight vs. a healthy weight. Partners working and learning together has strengthened and developed relationships between health, education and communities. The partnerships have been critical to improving the health of our students in MB. The second cycle of the YHS will be implemented in all Manitoba schools this fall (2012). Citation Format: Jane Griffith, Tannis Erickson, Katherine Fradette, Oliver Bucher, Carly Leggett, Kate McGarry, Elizabeth Harland. Raising healthy youth: Using the Manitoba Youth Health Survey to identify predictive factors of childhood obesity. [abstract]. In: Proceedings of the Eleventh Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2012 Oct 16-19; Anaheim, CA. Philadelphia (PA): AACR; Cancer Prev Res 2012;5(11 Suppl):Abstract nr B74.

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)0.010
metaresearch head score (Gemma)0.001
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 score0.052
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.300
GPT teacher head0.497
Teacher spread0.197 · 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

Citations0
Published2012
Admission routes2
Has abstractyes

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