Anxiety, Post-Traumatic Stress, and Burnout in Health Professionals during the COVID-19 Pandemic: Comparing Mental Health Professionals and Other Healthcare Workers
Bibliographic record
Abstract
The psychological impact of the pandemic on healthcare workers has been assessed worldwide, but there are limited data on how mental health professionals (MHPs) have been affected. Thus, this paper aims to investigate anxiety, post-traumatic stress, and burnout in a sample of MHPs. We conducted a descriptive, cross-sectional study on 167 participants: 56 MHPs, 57 physicians working closely with COVID-19 patients, and 54 physicians not working closely with such patients. MHPs reported good overall mental health. Most MHPs reported no post-traumatic stress, and their scores were significantly lower compared to HPs working closely with COVID-19 patients. MHPs' hyperarousal scores were also significantly lower compared to HPs working closely with COVID-19 patients, while their intrusion scores were statistically significantly lower than those of all other professionals. Multivariable logistic regressions showed that MHPs had lower odds of exhibiting state anxiety and low personal accomplishment compared to HPs not working closely with COVID-19 patients. In sum, MHPs seem to show almost preserved mental health. Thus, given the high mental healthcare demand during a pandemic, it would be useful to rely on these professionals, especially for structuring interventions to improve and support the mental health of the general population and other healthcare workers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".