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Record W2903159427 · doi:10.1186/s12966-018-0754-5

Examining changes in school vending machine beverage availability and sugar-sweetened beverage intake among Canadian adolescents participating in the COMPASS study: a longitudinal assessment of provincial school nutrition policy compliance and effectiveness

2018· article· en· W2903159427 on OpenAlexafffundabout
Katelyn Godin, David Hammond, Ashok Chaurasia, Scott T. Leatherdale

Bibliographic record

VenueInternational Journal of Behavioral Nutrition and Physical Activity · 2018
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of Waterloo
FundersInstitute of Population and Public HealthInstitute of Nutrition, Metabolism and DiabetesCanadian Institutes of Health ResearchInstitute of Neurosciences, Mental Health and AddictionPublic Health AgencyPublic Health Agency of Canada
KeywordsClinical nutritionCompliance (psychology)Behavioural sciencesCompassEnvironmental healthMedicineFood sciencePsychologySocial psychologyBiologyGeography

Abstract

fetched live from OpenAlex

BACKGROUND: School nutrition policies can encourage restrictions in sugar-sweetened beverage (SSB) availability in school food outlets in order to discourage students' SSB intake. The main objective was to examine how beverage availability in school vending machines changes over three school years across schools in distinct school nutrition policy contexts. Secondary objectives were to examine how students' weekday SSB intake varies with time and identify longitudinal associations between beverage availability and SSB intake. METHODS: This longitudinal study used data from the COMPASS study (2013/14-2015/16), representing 7679 students from 78 Canadian secondary schools and three provincial school nutrition policy contexts (Alberta - voluntary guidelines, Ontario public - mandatory guidelines, and Ontario private schools - no guidelines). We assessed availability of 10 beverage categories in schools' vending machines via the COMPASS School Environment Application and participants' intake of three SSB varieties (soft drinks, sweetened coffees/teas, and energy drinks) via a questionnaire. Hierarchical regression models were used to examine whether: i) progression of time and policy group were associated with beverage availability; and, ii) beverage availability was associated with students' SSB intake. RESULTS: Ontario public schools were significantly less likely than the other policy groups to serve SSBs in their vending machines, with the exception of flavoured milks. Vending machine beverage availability was consistent over time. Participants' overall SSB intake remained relatively stable; reductions in soft drink intake were partially offset by increased sweetened coffee/tea consumption. Relative to Ontario public schools, attending school in Alberta was associated with more frequent energy drink intake and overall SSB intake whereas attending an Ontario private school was associated with less frequent soft drink intake, with no differences in overall SSB intake. Few beverage availability variables were significantly associated with participants' SSB intake. CONCLUSIONS: Mandatory provincial school nutrition policies were predictive of more limited SSB availability in school vending machines. SSB intake was significantly lower in Ontario public and private schools, although we did not detect a direct association between SSB consumption and availability. The findings provide support for mandatory school nutrition policies, as well as the need for comprehensive school- and broader population-level efforts to reduce SSB intake.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.028
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.070
GPT teacher head0.392
Teacher spread0.321 · 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 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

Citations21
Published2018
Admission routes3
Has abstractyes

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