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Record W7065838911

An examination of immigrant status and association with childhood obesity

2014· dissertation· en· W7065838911 on OpenAlexaffabout

Bibliographic record

VenueMspace (University of Manitoba) · 2014
Typedissertation
Languageen
FieldEngineering
TopicOptical Systems and Laser Technology
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsOverweightObesityImmigrationLogistic regressionConfoundingChildhood obesityAssociation (psychology)Neighbourhood (mathematics)Child obesity
DOInot available

Abstract

fetched live from OpenAlex

Research regarding childhood obesity in Canadian children has failed to address the effects of immigration on weight status. This study examined correlates of obesity and overweight including family functioning, parenting style (consistent parenting, positive interactions, hostile interactions, and punitive parenting), neighborhood conditions, physical activity, and screen time in immigrant children (i.e., children not born in Canada but currently residing in Canada). Correlates of obesity were examined using the National Longitudinal Survey of Children and Youth (NLSCY), a nationally representative data set with several waves of data collection, conducted from 1994 to 2008. The correlates were analyzed using multiple regression models. Neighbourhood factors, family functioning and other parenting factors such as: hostile interaction, positive interactions, punitive parenting and consistent parenting, were not associated with BMI or obesity and overweight status. Contrary to previous findings, time spent in Canada was not associated with physical activity or screen time among immigrant children. None of the variables investigated were significantly associated with obesity and overweight status. This lack of significant findings may have been due to small immigrant sample sizes, inadequate or limited measures of confounding variables; such as macronutrient composition of diet that could not be accounted for in our analysis. However, given that models were run using both logistic and linear regression and results were consistent across the board, there may well have been no relationship between these variables. Findings were non-significant and therefore conclusive findings and recommendations could not be drawn from this study

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 score0.233
Threshold uncertainty score0.779

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 head0.003
GPT teacher head0.154
Teacher spread0.152 · 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
Published2014
Admission routes2
Has abstractyes

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