Factors associated with academic underachievement: a cross-sectional study in Atacora Northern Benin republic
Bibliographic record
Abstract
Aim This study aimed to identify factors associated with low academic performance among public primary pupils in Atacora Department, Benin Republic. Methods This cross-sectional study is conducted among pupils aged 8–14 years from public primary schools. Data on dietary diversity were collected using a 24-h dietary recall tool adapted from the FAO guidelines. Nutritional status was assessed through anthropometric measures and hemoglobin level, while cognitive abilities were assessed using Digit Span and Verbal fluency tests. The socio-economic, demographic and health characteristics were collected through digitalized questionnaires administered to pupils. Physical activity levels were measured using the Physical Activity Questionnaire for children (PAQ-C) Form. Academic performance was measured using end-of-month examination results provided by school boards. Results findings revealed that almost half (46.34%) of the pupils scored less than 10 out of 20 and were in fail category. Among pupils with normal growth according to WHO standards, cognitive factors as low Verbal fluency (OR = 3.119, p < 0.05); low Digit span (OR = 2.623, p < 0.05); nutritional factors as low dietary diversity (OR = 2.283, p < 0.05); socioeconomic conditions including paternal illiteracy (OR = 1.422, p < 0.05), and lack of household electricity (OR = 2.009, p < 0.05), and school related factors as long distance to school (OR = 3.187, p < 0.05), high level of absenteeism (OR = 1.052, p < 0.05), are predictors of academic underachievement. Conclusion Overall, Cognition, dietary diversity, access to electricity, pupils’ gender, distance to school, father’s literacy, are predictors of school performance in the study area. Integrated, context-sensitive policy interventions—spanning early childhood education, rural electrification, gender equity, parental engagement, school attendance, teacher training, nutritional support, and improved food accessibility—are crucial for enhancing academic performance in food-insecure regions of Northern Benin.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".