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Record W4399816037 · doi:10.3329/icmj.v13i1.73873

Factors Associated with Overweight/Obesity among Rural Secondary School Children in Bangladesh

2024· article· en· W4399816037 on OpenAlexaff
Shakira Tur Rahman, Md Nurul Amin, Md Jawadul Haque, Md Entekhab Ul Alam, Shathi Kumar, Rawson Kamal, Md Abu Syem, Shah Amanath Ullah, Nelofar Yasmin, Farhana Yasmin, Asadur Rahman, Shubhra Prakash Paul, Shitil Ibna Islam

Bibliographic record

VenueIbrahim Cardiac Medical Journal · 2024
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsOverweightObesityEnvironmental healthMedicinePediatricsInternal medicine

Abstract

fetched live from OpenAlex

Background & Objective: Unhealthy dietary patterns in combination with a sedentary lifestyle could be contributing to some major health issues worldwide. Obesity in children and adolescents can have short- and long-term adverse health consequences including early mortality. Although previously limited to urban areas, with the rapid adoption of urban lifestyle by the rural people the problem is now ubiquitous. It is difficult to develop a policy for promoting health and reducing obesity among adolescents living in rural regions since there is a lack of information on the factors that contribute to this problem. The present study was, therefore undertaken to evaluate the factors influencing overweight and obesity among rural adolescents. Methods: This cross-sectional study was done based on data collected by students of Rajshahi Medical College as part of their RFST (Rural Field-site Training) in 2019 from a rural area of Rajshahi, Bangladesh. A total of 535 students from two rural secondary schools participated in the study. Every alternate student of those schools from class VI-X was then included in the study as a respondent. The weight and height of the selected students were first taken followed by data collection on variables of interest. Using weight and height data, the body mass index (BMI) of the respondents was first determined and was plotted on a growth chart (recommended by CDC, Atlanta) to find the percentile. Then the nutritional status of the individual respondents was determined based on their percentile status and was classified as underweight, normal, overweight, or obese. Results: Almost one-fifth (18.2%) of the respondents was found overweight or obese. Upon comparing respondents’ demographic characteristics, food and exercise behaviour between overweight & obese and normal & underweight groups, the former group was found younger than the latter group. Respondents’ socioeconomic status (SES) was associated with their nutritional status with overweight & obese adolescents being significantly higher in the Middle- and rich SES group (78.4%) than in the poor and lower middle-class (61.4%). Neither dietary nor exercise behaviour was associated with the nutritional status of the respondents. Although the majority (84.2%) of normal & underweight adolescents preferred low to medium-calorie snacks, overweight and obese adolescents were more likely to choose high-calorie snacks. Fast walking and running were the more common forms of exercise behaviour and of longer duration among the overweight & obese cohort than those among normal & underweight cohorts. Conclusion: The study concluded that the period of early adolescence is vulnerable to developing obesity. Adolescents belonging to middle and well-off SES are more prone to be overweight or obese than those belonging to lower middle & poor SES. However, exercise, physical activity, and dietary habits of adolescents do not influence their nutritional status. Ibrahim Card Med J 2023; 13 (1&2): 19-25

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 score0.015
Threshold uncertainty score0.029

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 integrity0.0000.001
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 head0.009
GPT teacher head0.245
Teacher spread0.236 · 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
Published2024
Admission routes1
Has abstractyes

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