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Record W7127945182 · doi:10.64290/mauijef.v1i1.17

RELATIONSHIP BETWEEN ANXIETY AND ACADEMIC PERFORMANCE OF PRIMARY SCHOOL PUPILS IN SABON GARI, KADUNA STATE

2025· article· W7127945182 on OpenAlexaff
Umar Yusuf, Rabiu Aminu

Bibliographic record

VenueMAU INTERNATIONAL JOURNAL OF EDUCATIONAL FOUNDATIONS · 2025
Typearticle
Language
FieldSocial Sciences
TopicAfrican Education and Politics
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsAnxietySimple random sampleData collectionMental healthDescriptive researchDescriptive statisticsSample (material)Research design

Abstract

fetched live from OpenAlex

The researchers examined the relationship between Anxiety and Academic Performance of primary school pupils in Sabon Gari, Kaduna State. Three research objectives and three corresponding research questions guided the study, focusing on anxiety levels, contributing factors and academic outcomes. A descriptive survey design was adopted to enable the researchers to collect and analyze pupils’ anxiety levels and academic performance. The sample for the study comprised of One hundred and sixty (160) primary school pupils drawn from eight (8) schools out of twenty (20) schools in the study area using simple random sampling techniques. The instruments for data collection were structured questionnaires, and the responses were analyzed using mean and standard deviation. The results revealed a high prevalence of anxiety among primary school pupils in Sabon Gari, Kaduna State. The study also identified several academic-related factors that contribute to anxiety among these pupils. The findings suggest that anxiety during primary education may have long-term implications for pupils’ academic progression. The study recommends that government, schools, teachers, counselors, and parents implement mental health awareness programs to reduce stigma, improve understanding of anxiety, and encourage help-seeking behaviors among pupils.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.400
Teacher spread0.352 · 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.

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
Published2025
Admission routes1
Has abstractyes

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