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Record W4387806611 · doi:10.1371/journal.pone.0293316

Influence of diet quality on nutritional status of school-aged children and adolescents in Zanzibar, Tanzania

2023· article· en· W4387806611 on OpenAlexfundno aff
Fatma Saïd, Ahmed Gharib Khamis, Asha Salmin, Shemsa Nassor Msellem, Kombo Mdachi, Ramadhani Noor, Germana Leyna, Geofrey Mchau

Bibliographic record

VenuePLoS ONE · 2023
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
FundersOntario Council on Graduate Studies, Council of Ontario Universities
KeywordsMedicineTanzaniaOverweightEnvironmental healthMalnutritionBody mass indexObesityCross-sectional studyLogistic regressionPediatricsDemographyGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Malnutrition among young children and adolescents poses a serious health challenge in developing countries which results in many health problems during adulthood. Poor diet quality is known as the root cause of malnutrition which is caused by unhealthy food choices and bad eating habits among young children and adolescents. However, limited evidence is available on diet quality and its association with nutrition status among young children and adolescents in Zanzibar. This study examined the diet quality and its relationship with the nutritional status of school-aged children and adolescents in Zanzibar. METHODS: Data for this study was obtained from the cross-sectional survey of School Health and Nutrition (SHN) conducted in Zanzibar. The survey recruited children aged 5-19 years from 93 schools in Zanzibar. A seven-day food frequency questionnaire (FFQ) was used to assess dietary intake. Prime Dietary Quality Score (PDQS) consisted of 21 food groups was then constructed to assess the diet quality of school-aged children and adolescents. Body mass index (BMI-for-age Z-score) was used as the indicator of nutrition status. Both linear and logistic regression analysis techniques were used to determine the associations between BMI and PDQS. RESULTS: A total data of 2,556 children were enrolled in the survey. The prevalence of thinness was 8.1%, normal 82.1%, overweight 7.2% and obesity 2.6%. The mean (SD) PDQS score was 18.8 (3.2) which ranged from 8 to 33. Consumptions of green leafy vegetables (49.3%), yellow or red fruits (37.8%), legumes (38.3%), fish (36.3%), and vegetable oil (31.5%) were up to three times per week, whereas consumptions of white vegetables (77.3%), cooked vegetables (32.6%), citrus fruits (66.8%), other types of fruits (66.2%), nuts (46.4%), poultry (49.6%), whole grains (61%) and eggs (67.8%) were less than once per week. In terms of unhealthy foods, eating fried foods was reported by 26.3% up to three times per week, and 31.5% reported consuming sweets and ice cream up to three times in the past week. High PDQS was significantly associated with a reduction in BMI of children (p< 0.005). For each unit increase in the consumption of unhealthy foods such as fried foods, cooked vegetables and refined grains there is a significant increase in BMI. The odds of being obese decrease significantly as diet quality increases from the first to third quintile of PDQS (AOR = 0.2, 0.04-0.89 95% CI, p = 0.035). CONCLUSION: Consumption of high quality diet was found to be associated with a reduction in excessive weight among school-aged children and adolescents in Zanzibar. There is a need for interventions targeting to reduce unhealthy food consumption in school environment. Further research should be conducted to assess diet quality using PDQS among young children and adolescents.

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)0.001
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 score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.291
Teacher spread0.251 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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