Prevalence and factors associated with burnout syndrome in Peruvian health professionals before the COVID-19 pandemic: A systematic review
Bibliographic record
Abstract
Introduction: Burnout syndrome (BS) is a prevalent occupational health problem in health professionals. To describe the prevalence and factors associated with BS in Peruvian health professionals. Method: A systematic review and meta-analysis were performed. The key terms "burnout" and "professional exhaustion" were used with words related to Peru. The databases consulted were LILACS/Virtual Health Library, Medline/PubMed, Science Direct, EBSCO, Scopus, SciELO, and RENATI-SUNEDU; articles published between January 2000 to December 2020 were considered for inclusion. Methodological quality was evaluated using the Newcastle-Ottawa scale. Results: Thirty studies were identified (8 scientific articles and 22 graduate theses). The median sample size was 78, with an interquartile range of 50-110. A meta-analysis was performed to calculate a dichotomic prevalence of burnout syndrome in health professionals of 25 % (95%CI: 9 %-45 %; I2 = 97.14 %; 5 studies). Also, our meta-analysis estimated the overall prevalence of mild burnout (27 %; 95%CI: 16%-41 %; I2 = 96.50 %), moderate burnout (48 %; 95%CI: 32%-65 %; I2 = 97.54 %), and severe burnout (17 %; 95%CI: 10%-24 %; I2 = 92.13 %; 18 studies). We present meta-analyses by region, profession, hospital area, and by dimension of the Maslach Burnout Inventory. Overall, the studies presented adequate levels of quality in 96.7 % of the included studies (n = 29). In addition, our narrative review of factors associated with BS and its three dimensions identified that different studies find associations with labor, socio-demographic, individual, and out-of-work factors. Conclusions: There is a higher prevalence of moderate BS in Peruvian health professionals at MINSA and EsSalud hospitals in Peru, with severity differing by region of Peru, type of profession, work area, and dimensions of BS.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".