The influence of workplace stressors on the risk of cardiovascular diseases among healthcare providers: a systematic review
Bibliographic record
Abstract
Background: Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Healthcare workers are at increased risk due to workplace stressors such as long hours, shift work, and high job demands, which may worsen both modifiable and non-modifiable CVD risk factors. This systematic review examines the impact of these workplace stressors on the risk for CVD among healthcare providers. Methods: We conducted a systematic review of observational studies from inception to January 2024, following PRISMA guidelines. We searched databases including PubMed, Embase, Scopus, Web of Science, and PsycINFO using keywords related to workplace stressors and CVDs among healthcare professionals. The quality of the studies was assessed using the Newcastle-Ottawa Scale (NOS). Results: Our review included 31 observational studies (15 cohort studies, 13 cross-sectional studies, and three case-control studies) with a total of 323,978 participants from 17 countries. The key stressors identified were long working hours, night shifts, and high job strain. Most studies reported significant associations between these stressors and increased risks of hypertension, ischemic heart disease, and cardiometabolic disorders. The quality of the studies ranged from fair to good, indicating a low risk of bias. Conclusion: Growing evidence suggests a strong correlation between workplace stressors and an increased risk of cardiovascular disease among healthcare workers. This leads to negative consequences that affect their performance and may extend to the quality of their patients' care. Addressing these stressors through targeted interventions is crucial for protecting their health and improving patient care outcomes.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".