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Record W4281251153 · doi:10.1016/j.appet.2022.106092

Trajectories of beverage consumption during adolescence

2022· article· en· W4281251153 on OpenAlexafffundabout
Radhouene Doggui, Stéphanie Ward, Claire Johnson, Mathieu Bélanger

Bibliographic record

VenueAppetite · 2022
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsVitalité Health NetworkUniversité de MonctonUniversité de Sherbrooke
FundersFondation de la recherche en santé du Nouveau-Brunswick
KeywordsConsumption (sociology)Environmental healthWater consumptionPsychological interventionFruit juiceAge groupsMedicineFood sciencePsychologyDemographyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Beverages contribute substantially to daily energy and nutrient intakes. However, little is known about the co-development of beverage consumption throughout adolescence. This study aimed to investigate the presence of naturally occurring sub-groups of girls and boys following distinct trajectories of various types of beverage consumption (i.e. sugary beverages, tea and coffee, water, and milk) throughout adolescence. During the Monitoring Activities for Teenagers to Comprehend their Habits study, data were collected from 744 Canadian youths followed for six years (2013-2019). The participants were asked yearly (start-age 10-11 years old) to report how many times they consumed sugary beverages, tea and coffee, water, and milk in a week. Trajectories of beverage consumption were identified from age 11 to 18 using a person-centred approach, namely group-based multi-trajectory modelling. For girls, three different groups were identified: 'Water consumers' (62.7%), 'High beverage consumers' (20.9%), and 'Water and milk consumers' (16.4%). For boys, four different groups were identified: 'Water consumers' (39.1%), 'Water and milk consumers' (30.5%), 'Sugary drinks, coffee and tea consumers' (20.1%), and 'High beverage consumers' (10.4%). This study illustrates the complexity of beverage consumption patterns in adolescence. Various types of public health messaging and interventions may be required to promote healthier beverage consumption patterns among all adolescents.

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 score0.059
Threshold uncertainty score0.368

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 communication0.0000.000
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 head0.014
GPT teacher head0.252
Teacher spread0.238 · 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

Citations12
Published2022
Admission routes3
Has abstractyes

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