Anxiety and stress among healthcare professionals during COVID-19 in Ethiopia: systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: This study intended to assess the impact of COVID-19 pandemic on anxiety and stress among healthcare professionals in Ethiopia. DESIGN: This study applied a design of systematic review and meta-analysis of observational studies. DATA SOURCES: ELIGIBILITY CRITERIA AND OUTCOMES: Observational studies examining anxiety and stress among healthcare professionals in Ethiopia following COVID-19 pandemic were considered. The primary outcomes were the prevalence of anxiety and stress and the secondary outcomes were factors associated to the prevalence of anxiety and stress. DATA EXTRACTION AND SYNTHESIS: Two authors extracted the data and performed quality assessment independently. The Newcastle-Ottawa Scale was used to evaluate the quality of eligible studies. Random-effect model with the inverse variance method was used to estimate the pooled effect size of the outcome variables with its 95% CI. Publication bias was checked by DOI plot and Luis Furuya Kanamori index. Stata V.14.0 (StataCorp) software was used for statistical analysis. RESULTS: =97.85%, p<0.001)). Age, sex, marital status, working department, history of contact with confirmed COVID-19 cases and profession were associated factors for high level of anxiety and stress. CONCLUSIONS: COVID-19 pandemic highly affects mental health of healthcare professionals in Ethiopia. Anxiety and stress were among reported mental health problems among healthcare professionals during the pandemic. Timely psychological counselling programmes should be applied for healthcare professionals to improve the general mental health problems. PROSPERO REGISTRATION NUMBER: CRD42022314865.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".