Emergency Response to the COVID-19 Pandemic of the King Abdulaziz University in Jeddah: A Report on Stakeholder’s Opinions
Bibliographic record
Abstract
The devastating effect of COVID-19 has impacted global citizens for the past three years. More than six hundred and forty-six million people have been infected and there have been almost seven million casualties. Consequently, new variants have been discovered in quick succession around the world. Global communities have witnessed cruel fatalities and lost properties and businesses, and experienced the usual activities of service sectors being hampered, including those of post-secondary educational institutions, and the consequences of the COVID-19 pandemic ultimately damaged family life and society in general. Emergency management strategies were adopted by educational institutions around the world, including in the Middle East, in order to manage the ongoing pandemic. This study aimed to evaluate the emergency response mechanisms to COVID-19 at the King Abdulaziz University (KAU) by interviewing major stakeholders to ascertain their opinions through a cross-sectional survey. A total of 350 responses were recorded from students (64.28%), faculty members (21.42), and staff (14.28). The collected data were analyzed using statistical methods and illustrated using different schemes, graphs, and diagrams. Interestingly, the KAU emergency response plan for COVID-19 was appreciated by the respondents and it has emerged as a success story at a post-secondary educational institution in the KSA.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".