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Record W4214934041 · doi:10.2196/37304

Assessment of Preparedness for the COVID-19 Pandemic in Schools in Al-Rusafa District, Baghdad, Iraq, 2021

2022· article· en· W4214934041 on OpenAlex
Marha Kamoona, Deepak Kumar, Alison Yoos, Bashar Abdul Latif, Ayad AL-Temeemy, Safaa Al Ghanimy

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIproceedings · 2022
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsnot available
Fundersnot available
KeywordsPreparednessGovernment (linguistics)PandemicPopulationStratified samplingObservational studyMedicineCoronavirus disease 2019 (COVID-19)Sample (material)Family medicineEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

Background Following the international spread of the novel coronavirus (SARS-CoV-2) or COVID-19 pandemic, the Iraqi government took several steps to prevent community transmission, including the indefinite closure of schools as a measure to safeguard schoolchildren from COVID-19. The key rationale behind these decisions was the insufficient preparedness level within schools to prevent infection and the lack of appropriate vaccines for children. Objective Researchers assessed COVID-19 preparedness levels in schools in Al-Rusafa district, Baghdad, to prepare schools for reopening. Methods An observational study design was conducted to assess the schools. Stratified sampling was performed to make the sample more representative; we stratified the schools into 3 categories based on sex, level (primary or secondary), and administration (public or private). The study population comprised all students and teachers in the selected sample. The assessment was carried out retrospectively for 3 months, from May 31, 2021. Data were collected through face-to-face interviews and analyzed using Microsoft Excel. Tables and pie charts were used to display the results. Results The assessment was completed in 40 schools—20 (50%) primary schools, 10 (25%) high schools, 6 (15%) intermediate schools, and 4 (10%) secondary schools. Overall, the assessment covered 1162 teachers and 16,776 students. The highest infection rate, according to school category, was among primary school staff (6.14%). Moreover, 92% (n=39) of the schools did not have a contact number for a nearby ambulance, and early detection system was weak in 60% (n=24) of the schools, which reflected low levels of school participation in preparing against the COVID-19 pandemic. Referral system for any sick person to an appropriate health facility was not present or was disabled in 63% (n=25) of the schools. Conclusions The assessment concluded that none of the schools had a robust screening system to record students infected with COVID-19. The study discusses several actions and requirements that should be reviewed and addressed to prevent the spread of COVID-19 in the schools and the community.

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.

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)0.004
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.278
GPT teacher head0.476
Teacher spread0.198 · 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