Post-COVID-19 depression prevalence in Iranian nurses: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: This systematic review and meta-analysis aimed to assess the pooled prevalence of post-COVID-19 depression among Iranian nurses, identify at-risk groups and provide practical recommendations for intervention. DESIGN: In adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review and meta-analysis, encompassing studies published from 2019 to 2024. Comprehensive searches were performed across international and Iranian databases. DATA SOURCES: PubMed, Scopus, Web of Science, Google Scholar and Scientific Information Database. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Studies meeting the following criteria were included in the analysis: (1) conducted on the population of Iranian nurses, (2) keywords explicitly included in the title or abstract, (3) studies published between 2019 and 2024, (4) published in Persian or English and (5) reported the prevalence of depression either in the entire population or differentiated by gender. DATA EXTRACTION AND SYNTHESIS: Data extraction was conducted independently by two reviewers, and quality assessment was performed using the Newcastle-Ottawa Scale. Statistical analyses were executed using random effects models to estimate pooled prevalence rates, with subgroup analyses and sensitivity tests conducted to explore sources of heterogeneity and confirm result robustness. RESULTS: A total of 22 studies met the inclusion criteria, capturing data from various provinces across Iran. The pooled prevalence of depression among Iranian nurses post-COVID-19 was estimated at 23% (95% CI 19% to 30%), indicating a substantial mental health burden within this population. Subgroup analyses revealed notable disparities in depression rates across demographic and professional characteristics. Nurses holding advanced degrees exhibited a higher mean depression score (13.33, 95% CI 9.48 to 16.74) compared with those with bachelor's degrees. Male nurses also reported slightly higher depression scores (12.04, 95% CI 7.58 to 16.50) than their female counterparts. Furthermore, moderate depression emerged as the most common severity level, affecting 24% of nurses. Sensitivity analyses demonstrated that no single study disproportionately influenced the pooled estimates, reinforcing the reliability of the findings. CONCLUSIONS: This review and meta-analysis illuminate the mental health challenges faced by Iranian nurses in the wake of COVID-19. With a significant proportion of nurses experiencing depression, addressing their psychological needs is imperative. Tailored interventions, such as stress management workshops, access to professional counselling and workplace policies that prioritise mental health, are essential to enhance resilience and sustain healthcare quality during future public health crises. Efforts must also focus on structural changes to create a supportive environment that fosters well-being and professional satisfaction among nurses, ultimately improving patient outcomes and overall healthcare system performance.
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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 | 0.026 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.042 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".