The Effect of the COVID-19 Pandemic on Health Care Workers’ Anxiety Levels: Protocol for a Meta-Analysis
Bibliographic record
Abstract
Background The COVID-19 pandemic has been declared a public health emergency of international concern; this has caused excessive anxiety among health care workers. In addition, publication bias and low-quality publications have become widespread, which can result in the dissemination of unreliable findings. Objective This paper presents the protocol for a meta-analysis with the following two aims: (1) to examine the prevalence of anxiety among health care workers and determine whether it has increased due to the COVID-19 pandemic, and (2) to investigate whether there has been an increase in publication bias. Methods All related studies that were published/released from 2015 to 2020 will be searched in electronic databases (Web of Science, PubMed, PsyArXiv, and medRxiv). The risk of bias in individual studies will be assessed using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist. The heterogeneity of the studies will be assessed using the I2 statistic. The effect size (prevalence rates of anxiety) and a 95% CI for each paper will also be calculated. We will use a moderator analysis to test for the effect of COVID-19 on health care workers’ anxiety levels and detect publication bias in COVID-19 studies. We will also assess publication bias using the funnel plot and Egger regression. In case of publication bias, if studies have no homogeneity, the trim-and-fill procedure will be applied to adjust for missing studies. Results Database searches will commence in November 2020. The meta-analysis will be completed within 2 months of the start date. Conclusions This meta-analysis aims to provide comprehensive evidence about whether COVID-19 increases the prevalence of anxiety among health care workers and whether there has been an increase in publication bias and a deterioration in the quality of publications due to the pandemic. The results of this meta-analysis can provide evidence to help health managers to make informed decisions related to anxiety prevention in health care workers. International Registered Report Identifier (IRRID) PRR1-10.2196/24136
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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 | 0.068 | 0.122 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.022 | 0.040 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.063 | 0.007 |
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".