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Record W4223935147 · doi:10.5539/jel.v11n3p64

Confronting COVID-19 Whilst Elementary School Students Resume In-Person Learning

2022· article· en· W4223935147 on OpenAlexvenueno aff
Doreen Ahwireng

Bibliographic record

VenueJournal of Education and Learning · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsDeskSocial distancePsychologyPsychological interventionCoronavirus disease 2019 (COVID-19)PandemicMedical educationQualitative researchClass (philosophy)PedagogyMedicineNursingSociologyEngineeringComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.065
GPT teacher head0.462
Teacher spread0.397 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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