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INTEGRAL APPROACHES TO TEACHING SENIOUR SCHOOLCHILDREN AND THEIR IMPACT ON DEVELOPMENT OF A HEALTHY LIFESTYLE

2016· article· en· W2560008837 on OpenAlexaff
Л. Н. Гончарова, Alexey P. Yurenev

Bibliographic record

VenueIntegration of Education · 2016
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Challenges
Canadian institutionsPurdue Pharma (Canada)
FundersRussian Foundation for Basic Research
KeywordsPsychologyHumanitiesMathematics educationPhilosophy

Abstract

fetched live from OpenAlex

Introduction: the deterioration of health indicators among adolescents is an alarming tendency observed recently. The need for development of health-safeguarding behaviour in high school students of Saransk is obvious. The authors analysed the general health status of this group depending on implementation of various types of educational programmes in high schools. Materials and Methods: the data of arterial blood pressure, body mass index, food habits among high school students according to age, gender, nationality of schoolchildren and level of integration into educational programmes have been analysed. The research included 203 high school students from14 to 17 years old, 57 % boys and 43 % girls (grades 9 to 11) from two different schools of Saransk city with different educational programmes. Results: the research demonstrated a positive impact of sport programmes on health-preserving behaviour of high school students, resulted in stabilisation of arterial blood pressure, normal body mass and lower level of fast food consumption. Discussion and Conclusions: educational programmes focused on acceptance and implementation of healthy lifestyle could be considered as possible factors affecting health-preserving behaviour. The authors suggest paying more attention to these programmes’ inclusion into educational process.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.268

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 head0.092
GPT teacher head0.331
Teacher spread0.239 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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