The Impact of COVID-19 on Mental Health of Healthcare workers in Saudi Arabia: A Review
Bibliographic record
Abstract
This study aimed to conduct a literature search to review the impact of COVID-19 on the mental health of healthcare workers in Saudi Arabia using statistical meta-analysis.This study also focused on the role of human resources in eliminating the pressure experienced by healthcare workers. .The work overload due to the pandemic has led to high stress levels and other mental health issues among healthcare workers.The literature search was conducted in January 2021, and records were reviewed from the Scopus database using the keywords COVID-19, mental health, and healthcare workers.A pool of 488 papers was considered, of which 481 focused on the pandemic's impact, and seven included the keywords Saudi Arabia and the COVID-19 pandemic.Relevant studies were included in the literature search.The pandemic impacted the mental health and well-being of healthcare workers significantly.The findings were divided into three sections: literature search, theoretical perspective, and statistical analysis through meta-analysis using Meta-Essentials software.This study suggests that mental health is pivotal for healthcare workers' well-being during the pandemic as it affects the well-being of society at large.This study also showed that appreciation and increased remuneration have improved the efficiency of healthcare workers in Saudi Arabia.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".