Confronting COVID-19 Whilst Elementary School Students Resume In-Person Learning
Bibliographic record
Abstract
Resuming in-person teaching and learning during the COVID-19 pandemic implies that schools must deploy strategies to enforce adherence to the safety protocols to help contain and reduce the spread of the corona virus disease among school children. Thus, the current qualitative study adopted a case study design to explore strategies that were deployed to enforce adherence to the COVID-19 safety protocols among elementary school students. A semi-structured interview guide was used to gather data from 30 teachers enrolled in a one-year master’s degree in Educational Leadership and Management program at a public university in Ghana. The study showed that strict and compulsory handwashing before entering the school was deployed to ensure adherence to handwashing safety protocol, provision of veronica buckets contributed to adherence to handwashing. Also, interventions that were deployed to enforce social distancing were spacing of desk, having mealtime in class, eating meals in turns, suspension of assembly and other social gatherings, split class for shift system. Additionally, schools ensured students wore nose masks by providing nose masks to students who could not afford.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".