The COVID 19 Pandemic: College Adolescents’ Perception on School Reopening in Nigeria
Bibliographic record
Abstract
BACKGROUND: Several colleges were closed in the wake of the COVID-19 pandemic. However, it is not clear if school closure has curbed the incidence of the infection. OBJECTIVES: This study aimed to determine the perception of adolescent college students on school reopening and associated factors. METHODOLOGY: This was a school-based cross-sectional study. A two-stage sampling technique was used to select five hundred adolescent college students from six secondary schools in the Enugu metropolis, Nigeria. Data were analysed with IBM Statistical Package for Social Sciences (SPSS) statistical software version 25. RESULTS: The mean age of the students was 15.1±1.7 years and the majority, 56.4% were females. A higher proportion of the respondents, 78.0% were willing to return to school. For those not willing to return to school, the major reason was the preference for homestay until the pandemic is over, 57.3%. The majority of the students, 67.6% had their learning improved during the pandemic. More than half of the students, 65.0% had online classes during the pandemic. Predictors of willingness to return to school amidst the COVID-19 pandemic included being a male student, (AOR=0.304, 95%CI: 0.189-0.489), and being from a family of high socio-economic class, (AOR=0.363, 95%CI: 0.154- 0.855). CONCLUSION: Closure of schools should be revisited, with enforcement of all preventive measures. Alternative methods for education such as e-learning seem to create a divide between the rich and the poor. It is therefore pertinent to develop a bridging plan to fill the gap created by this divide.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".