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Record W2888114097 · doi:10.1002/oby.22218

Correlates of Weight‐Loss Methods Among Young Adults in Canada

2018· article· en· W2888114097 on OpenAlexafffundabout
Amanda Raffoul, David Hammond

Bibliographic record

VenueObesity · 2018
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of Waterloo
FundersCanadian Institutes of Health ResearchUniversity of WaterlooPublic Health AgencyPublic Health Agency of Canada
KeywordsWeight lossOverweightObesityMedicineDemographyHealth literacyYoung adultGerontologyBody mass indexHealth care

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to examine the prevalence of various weight-loss behaviors among young adults in Canada and differences in the use of these methods by demographic characteristics, health literacy, and perceived body size. METHODS: Data from the 2016 wave of the Canada Food Study were used, which collected self-reported information from 3,000 young adults in five cities. Linear regression models were conducted to investigate correlates of the number and type of weight-loss methods used across the following four categories: dietary changes, physical activity, assisted weight-loss methods, and unhealthy behaviors. RESULTS: In the past 12 months, more than half of respondents reported a weight-loss attempt, and nearly one-fifth engaged in an unhealthy weight-loss method. The risk of engaging in a greater number of weight-loss behaviors across categories was higher for women, nonbinary-gendered individuals, and individuals who perceived themselves as having overweight or obesity. Respondents with lower health literacy engaged in a significantly greater number of unhealthy methods. CONCLUSIONS: Many young adults use healthier weight-loss strategies, but a concerning number use multiple and/or unhealthy weight-loss methods as well. Furthermore, there are subgroup differences in weight-loss method engagement, which holds significance for public health efforts aiming to improve weight-related behaviors.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.422
Teacher spread0.387 · 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 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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