Image_2_Burnout in residents during the first wave of the COVID-19 pandemic: a systematic review and meta-analysis.pdf
Bibliographic record
Abstract
Introduction<p>The high prevalence of burnout in resident physicians is expected to have increased as a result of the expansion of the pandemic. We conducted a systematic review with a meta-analysis of studies conducted during the first wave of the COVID-19 pandemic on burnout in residents and potential associated risk factors.</p>Methods<p>The search was done in the Web of Science, MEDLINE, Scopus, and Lillac databases (April 2020–October 2021) using a priori protocol based on the PRISMA guidelines. The Newcastle Ottawa Scale was used to assess the risk of bias in the included studies. We estimated the pooled prevalence (95% CI) of burnout and the prevalence ratio (95% CI) of each risk factor associated.</p>Results<p>We included 23 studies from 451 potential initial articles and those written in the English language; all of the collected studies were cross-sectional with anonymous online surveys, involving 4,998 responders (34%), of which 53.2% were female responders, 51% were R1-2, and 71% were in direct contact with COVID-19 patients. Eighty-seven percent presented a low-to-moderate risk of bias. Publication bias was not shown. The estimated pooled prevalence of burnout was 40% (95% CI = 0.26 – 0.57). Burnout was associated with psychiatry history (PR = 4.60, 95% CI = 1.06 – 20.06). There were no differences by gender, civil status, children in-charge, year of residency, or time exposure to COVID-19.</p>Discussion<p>The overall prevalence of burnout in residents during the first wave of the pandemic was in line with the results described in this collective before the pandemic. The presence of a psychiatry history was a potential burnout risk factor, suggesting a high vulnerability during the peak of the stress period and the need to implement mental health surveillance for this subgroup.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.040 | 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 teacher head, 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".