Exploring the Mental Health Challenges of Emergency Medicine and Critical Care Professionals: A Comprehensive Review and Meta-Analysis
Bibliographic record
Abstract
Burnout and depression are global problems affecting healthcare providers, especially those working in stressful departments such as emergency departments (EDs) and critical care units (CCUs). However, pooled data analysis comparing healthcare providers operating in the ED and CCU is yet to be conducted. Therefore, this meta-analysis was systematically conducted to investigate and compare the prevalence of burnout and depression among emergency medicine (EM) and critical care medicine (CCM) professionals. We systematically searched for articles related to our research topic using the database search method and manual search method, which involved reviewing the reference lists of articles from electronic databases for additional studies. After screening the literature from the databases using the eligibility criteria, a quality appraisal using the Newcastle-Ottawa scale was performed on the eligible studies. In addition, a meta-analysis using the Review Manager software was performed to investigate the prevalence rates of burnout and depression. A total of 10 studies with 1,353 EM and 1,250 CCM professionals were included for analysis in the present study. The pooled analysis did not establish any considerable differences between EM and CCM healthcare workers on the prevalence of high emotional exhaustion (EE) (odds ratio (OR) = 1.01; 95% confidence interval (CI) = 0.46-2.19; p = 0.98), high depersonalization (OR = 1.16; 95% CI = 0.61-2.21; p = 0.64), low personal accomplishment (PA) (OR = 0.87; 95% CI = 0.67 - 1.12; p = 0.28), and depression (OR = 1.20; 95% CI = 0.74-1.95; p = 0.45). Moreover, pooled data showed no considerable differences in EE scores (mean difference (MD) = -1.07; 95% CI = -4.24-2.09; p = 0.51) and depersonalization scores (MD = -0.31; 95% CI = -1.35-0.73; p = 0.56). However, EM healthcare workers seemed to have considerably lower PA scores than their CCM counterparts (MD = 0.12; 95% CI = 0.08-0.16; p < 0.00001). No considerable difference was recorded in the prevalence of burnout and depression between EM and CCM healthcare workers. However, our findings suggest that EM professionals have lower PA scores than CCM professionals; therefore, more attention should be paid to the mental health of EM professionals to improve their PA.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".