Factors associated with suicidal ideation in healthcare personnel: a systematic review
Bibliographic record
Abstract
Aim: This paper investigates suicidal ideation among healthcare professionals, a growing concern that affects their mental well-being and the quality of healthcare delivery. The study aims to identify key risk factors, such as work-related stress, exposure to death, and lack of institutional support, that contribute to suicidal ideation in this population. It also explores protective factors, including resilience, social support, and institutional resources, that may mitigate these risks. Method: A systematic review was conducted on studies published between 2020 and 2024. The literature search spanned databases such as PubMed, Scopus, Web of Science, PsycINFO, Dialnet, and Scielo. The review followed the PRISMA guidelines to ensure thoroughness and transparency in study selection. To assess the quality of the included studies, standardized tools like the Newcastle-Ottawa Scale were applied. Results: The review identified that the COVID-19 pandemic has intensified factors leading to suicidal ideation among healthcare professionals, with a notable increase in prevalence during this period. Identified risk factors included high levels of occupational stress, frequent exposure to death and suffering, and insufficient institutional support. Conversely, protective factors like resilience, social support, and access to institutional resources were found to reduce susceptibility to suicidal ideation. Conclusion: The findings highlight an urgent need for comprehensive prevention strategies and support programs targeting healthcare personnel. Recommendations for interventions span individual, organizational, and public policy levels. Enhancing resilience and providing institutional support could be crucial steps in reducing the incidence of suicidal ideation in this vulnerable group, ultimately improving both their mental health and the quality of healthcare services.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".