Post-COVID-19 depression prevalence in Iranian nurses: a systematic review and meta-analysis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,026 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,021 | 0,042 |
| Bibliométrie | 0,010 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».