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Record W2553987284 · doi:10.20286/nova-jmbs-050301

Body mass index and its relationship with socio-economic variables in Schoolgirls

2016· article· en· W2553987284 on OpenAlexvenueno aff
Purreza Abolghasem, Leila Dehghankar, Moslem Jafarisani, ali Pouryosef, Hamidreza Tadayyon, Ali Khalafi

Bibliographic record

VenueNova Journal of Medical and Biological Sciences · 2016
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsBody mass indexAnthropometryIndex (typography)Affect (linguistics)DemographyBody heightSocioeconomic statusPsychologyMedicineBody weightSociologyPopulation

Abstract

fetched live from OpenAlex

Background: Evaluation of anthropometric and body mass index is not only widely used to assess children's development, but also is simplicity, affordability and reliability and most powerful tool to study the growth and development of children in different societies. BMI is one of the most important indicators of the growth, especially in childhood, which can be influenced by some socio - economic variables. The present study aimed to investigate the relationship between body mass index with some of the socio - economic conditions in children with a primary and secondary education to physical development factors and social - economic conditions affecting the BMI.Methods: This was a cross-sectional study and 200 children between 7 to 17 years of primary and secondary schools were selected and divided into two groups. Tools used in this study were questionnaires, Balance and Tape measure. Data were analyzed by SPSS 20 software. P<0.05 considered statiscally significant.Results: Majority of the samples was in normal and thin range. High school student girls had a lower average body mass index, and percentage of weight loss was greater among them.Conclusion: Socio-economic variables affect the lifestyle of families and they can be associated with BMI. Keywords: Body mass index, female students, parents socio-economic variables.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.311

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 studies0.0000.001
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 head0.066
GPT teacher head0.321
Teacher spread0.255 · 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
Published2016
Admission routes1
Has abstractyes

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