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Record W3185737480 · doi:10.1371/journal.pone.0254693

Factors associated with school achievement of children aged 8–10 years in rural Bangladesh: Findings from a post hoc analysis of a community-based study

2021· article· en· W3185737480 on OpenAlexfundno aff
Sheikh Jamal Hossain, Fahmida Tofail, Hasan Mahmud Sujan, Shams El Arifeen, Jena Hamadani

Bibliographic record

VenuePLoS ONE · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
FundersGlobal Affairs CanadaDepartment for International DevelopmentInternational Centre for Diarrhoeal Disease Research, BangladeshStyrelsen för Internationellt Utvecklingssamarbete
KeywordsPost-hoc analysisMedicineGerontologyPediatricsDemographyPsychologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Education is one of the most important human capitals. Investment in education at early age returns best. A lot of factors influence children's educational achievement. Studies in developed countries well established the relation of school achievement with its associated variables. But information is lack on what factors play important role for school achievement at early age in low resource settings like Bangladesh. We aimed to find factors associated with school achievement in rural Bangladesh. METHOD: The data were acquired from a long-term follow up study, conducted in 8-10 years old children (n = 372). We used a locally developed school achievement tool based on Wide Range Achievement Test-4 to measure reading, spelling and math computation, Wechsler abbreviated scale of intelligence to measure intelligence Quotient (IQ), Digit span forward and backward for short term memory, and locally available Strength and Difficulties Questionnaire to measure behaviour. Socioeconomic and anthropometric information of the mothers and children were also collected. Multicollinearity of the data was checked. Unadjusted and adjusted multiple linear regression analysis was performed. FINDINGS: Years of schooling and short-term memory were positively related to reading, spelling and math computation. For years of schooling it was-reading B = 8.09 (CI 5.84, 10.31), spelling 4.43 (4.33, 8.53) and math computation 5.23 (3.60, 6.87) and for short term memory- reading 3.56 (2.01,5.05), spelling 4.01 (2.56, 5.46) and math computation 2.49 (1.37, 3.62). Older children had lower scores of reading -0.48 (-0.94, -0.02), spelling -0.41 (-0.88, -0.02) and math computation -0.47 (-0.80, -0.14). Children's IQ predicted reading 0.48 (0.14, 0.81) and spelling 0.50 (0.18, 0.82) skills. Mother and father's education predicted Spelling 0.82 (0.16, 1.48) and reading 0.68 (0.06, 1.30) capacity respectively. Children enrolled in private schools had higher reading 10.28 (5.05, 15.51) and spelling 6.22 (1.31, 11.13) than those in the government schools. Children with more difficult behaviour tended to have lower scores in reading -0.51 (-0.96, -0.05). CONCLUSION: Children's school achievement is influenced by their IQ, years of schooling, type of school and parents' education. Therefore, intervention should be made to focus specifically on these variables and establish the effect of this intervention through robust research design.

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)0.002
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 score0.031
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.278
Teacher spread0.223 · 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
Published2021
Admission routes1
Has abstractyes

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