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Record W1970216597 · doi:10.3148/66.1.2005.12

<i>Does eating while watching television</i>influence children's food-related behaviours?

2005· article· en· W1970216597 on OpenAlexafffundvenueabout
Marie Marquis, Yves P. Filion, Fannie Dagenais

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversité de Montréal
FundersDanone Institute of Canada
KeywordsPsychologyEating behaviorFood intakeUnhealthy foodFront (military)Developmental psychologyMedicineObesityGeography

Abstract

fetched live from OpenAlex

To assess children's food-related behaviours and their relationships with eating while watching television (TV), data were collected from 534 ten-year-old French-Canadian children. A self-administered questionnaire was used. Almost 18% of girls and over 25% of boys reported eating in front of the TV every day. Although, overall, the boys' eating pattern was less healthy than the girls', all of the children's food choices deteriorated with increased frequency of eating in front of the TV. Compared with girls, boys gave more importance to coloured and attractive foods, and to selecting foods similar to those eaten by others. Over 50% of children reported always receiving negative weight-related comments from family members. For boys, significant correlations were found between the frequency of eating in front of the TV, the importance given to a food's appearance, and their requests to parents for advertised foods. Significance was at the p<0.05 level for all findings. These results suggest that gender should be considered in attempts to understand children's food motivations and behaviours. The findings also indicate the need to document children's eating environments, and to inform children and their families about eating behaviours that may be associated with a given environment.

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)0.004
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.035
GPT teacher head0.352
Teacher spread0.317 · 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 designNot applicable
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

Citations44
Published2005
Admission routes4
Has abstractyes

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