Understanding key factors contributing to mental health challenges among pediatric nurses: a systematic review
Bibliographic record
Abstract
Background: The mental health and wellbeing of nurses, particularly pediatric nurses, have garnered attention due to the increased risk of mental health challenges associated with their demanding profession. These nurses are especially vulnerable, yet their mental wellbeing is often understudied. Objective: This systematic review aims to identify and analyze key factors associated with mental health challenges among pediatric nurses and explore how these factors interact to influence their wellbeing. Methodology: The review protocol was registered in PROSPERO (CRD42024553062) and adhered to PRISMA guidelines. A comprehensive search was conducted across six databases: PubMed Scopus, CINAHL, Web of Science, Medline, and Embase. Eligible studies included both qualitative and quantitative studies that examined factors linked to mental health challenges among pediatric nurses. The quality of the studies was appraised using the Mixed Methods Appraisal Tool (MMAT). Data extraction and synthesis involved qualitative content analysis to identify key factors. Results: Five studies from China, Turkey, Greece, Canada, and Saudi Arabia were included. The key factors identified were high workload, poor work environment, limited resources, and strained interpersonal relationships, lack of support, irregular shift patterns, demanding roles, and financial strain. These factors were significantly associated with increased stress, burnout, anxiety, and depression among pediatric nurses. The interaction of these factors created a complex web influencing their mental health, with supportive work environments and adequate financial compensation mitigating some negative effects. Conclusion: This systematic review identifies high workload, poor work environment, limited resources, and strained interpersonal relationships, lack of support, irregular shift patterns, demanding roles, and financial strain as key factors impacting the mental wellbeing of pediatric nurses. These factors interact to exacerbate stress, burnout, anxiety, and depression. Effective interventions should include manageable nurse-to-patient ratios, adequate resource allocation, fostering a supportive work culture, flexible scheduling, targeted support for senior nurses, and improved financial compensation.
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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 | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| 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".