Hair cortisol change at COVID-19 pandemic onset predicts burnout among health personnel
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has put chronic pressure on worldwide healthcare systems. While the literature regarding the prevalence of psychological distress and associated risk factors among healthcare workers facing COVID-19 has exploded, biological variables have been mostly overlooked. METHODS: 467 healthcare workers from Quebec, Canada, answered an electronic survey covering various risk factors and mental health outcomes three months after the onset of the COVID-19 pandemic. Of them, 372 (80%) provided a hair sample, providing a history of cortisol secretion for the three months preceding and following the pandemic's start. We used multivariable regression models and a receiver operating characteristic curve to study hair cortisol as a predictor of burnout and psychological health, together with individual, occupational, social, and organizational factors. RESULTS: As expected, hair cortisol levels increased after the start of the pandemic, with a median relative change of 29% (IQR = 3-59%, p < 0.0001). There was a significant association between burnout status and change in cortisol, with participants in the second quarter of change having lower odds of burnout. No association was found between cortisol change and post-traumatic stress disorder, anxiety, and depression symptoms. Adding cortisol to individual-occupational-socio-organizational factors noticeably enhanced our burnout logistic regression model's predictability. CONCLUSION: Change in hair cortisol levels predicted burnout at three months in health personnel at the onset of the COVID-19 pandemic. This non-invasive biological marker of the stress response could be used in further clinical or research initiatives to screen high-risk individuals to prevent and control burnout in health personnel facing an important stressor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".