Determinants of Primary School Non-Enrollment and Absenteeism: Results from a Retrospective, Convergent Mixed Methods, Cohort Study in Rural Western Kenya
Bibliographic record
Abstract
BACKGROUND: Education is a key element in the socioeconomic development required to improve quality of life in Kenya. Despite the introduction of free primary education, primary school enrollment and attendance levels remain low. Drawing on qualitative and quantitative data, this study explores the determinants of non-enrollment and absenteeism in rural western Kenya and potential mitigation strategies to address these issues. METHODS: The study was conducted in Bwaliro village in rural western Kenya. A random sample of 64 students was obtained by blocking the village primary school's student population according to grade level, gender, and orphan status. Qualitative and quantitative data were collected through interviews with parents, guardians, and key informants, and focus group discussions with students. Quantitative data were compared using chi-square tests, Student's T-test, and Poisson regressions. Qualitative data were analyzed using thematic content analysis. RESULTS: Malaria, menstruation, and lack of money were among the most notable determinants of primary school dropout and absenteeism, and these factors disproportionately impacted orphans and female students. Potential mitigation strategies suggested by the community included provision of malaria treatment or prevention, reduction in education costs, expansion of the established school-feeding program, and provision of sanitary pads. CONCLUSION: Despite free primary education, numerous factors continue to prevent children in rural western Kenya from attending primary school. The findings suggest that interventions should primarily target orphaned and female students. Prior to implementation, suggested mitigation strategies should be assessed for cost-effectiveness.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".