Factors Affecting Pupils’ Absenteeism at Felicormfort Junior High School (JHS) in Cape Coast, Ghana
Bibliographic record
Abstract
This study aimed at finding out factors affecting pupils’ absenteeism at Felicomfort JHS at Amamoma within the University of Cape Coast, Ghana. The total population was 145 covering the JHS1, JHS2, JHS3 pupils and teachers of the school. Purposive sampling technique was used to select 34 respondents. These were made up of 10 out of 15 teachers, 10 parents out of 53 and 14 pupils out of their accessible population of 56. Pretest, posttest, questionnaires and interviews were used to collect data from respondents. Case study design was used for the study and data analysis was done, using mean values, frequency and percentage counts with the Predictive Analytical Software (PASW) version 21. Key findings of the study indicated that 71.4 percent of absenteeism was due to teachers’ inability to care and find out from pupils the cause of their absenteeism, while 70 percent of respondents indicated that parental lack of care was the major cause of their absenteeism. However, majority (10) of respondents (71%) disagreed that pupils’ attitudes were part of the contributory factors to their habitual absenteeism. The overall percentage mean (58%) representing 8 of the pupils discounted teacher factor as responsible for their absenteeism. It was recommended that government through the District Assemblies offer some financial assistance to poor and single parents to enable them to adequately cater for their wards at school.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".