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Record W7135449473

Eating habits and nutrition of school childern

2025· dissertation· cs· W7135449473 on OpenAlexaboutno aff
Petra Koutná

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2025
Typedissertation
Languagecs
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsOverweightQuarter (Canadian coin)Healthy eatingBody weightObesityEating behaviorFood habits
DOInot available

Abstract

fetched live from OpenAlex

The study focuses on the eating habits and nutrition of the pupils aged 11-15 years. It provides a detailed analysis of children's daily routines, including eating patterns, hydration, physical activity, sleep duration, and screen time. Particular emphasis is placed on the potential impact of school meals on the regularity, variety and quality of children's diets and its possible role in the prevention of overweight and obesity. The main objective was to investigate the extent to which school meals influence children's lifestyles and whether there are differences between those who eat at the school canteen and those who do not. The research was conducted through a questionnaire survey among six primary schools and students from five multi-year grammar schools in the Olomouc Region. The results showed that school meals contribute positively to the regularity of children's eating habits. Children attending the school canteen usually eat five times a day, compared to their peers who do not eat school meals, with a quarter of them eating only 1-2 times a day. When average weight was measured, it was confirmed that children who eat in the school canteen weigh on average 2.23 kg lower body weight than children who do not eat the school canteen. Two-thirds of respondents were found to have lack of...

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)0.000
metaresearch head score (Gemma)0.001
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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.256
Teacher spread0.248 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueDigital Repository (National Repository of Grey Literature)Same topicObesity, Physical Activity, DietFrench-language works237,207