Self-compassion, burnout, and biomarkers in a sample of healthcare workers during the COVID-19 pandemic: a cross-sectional correlational study
Bibliographic record
Abstract
Abstract Background Burnout among healthcare professionals is a serious problem with multiple consequences for the individuals and organizations affected. Thus, accessible and effective interventions are still needed to prevent and attenuate burnout. Self-compassion has recently been well supported in preventing and reducing burnout in various professions. Current research also demonstrated protective associations between self-compassion and well-being and/or psychological health indicators. Few studies are available on this topic during the COVID-19 pandemic or on healthcare workers from Quebec or Canada. Moreover, only a limited number of studies have looked at the associations of self-compassion with physiological variables. This cross-sectionnal correlational study attempts to evaluate the association between self-compassion and burnout, among healthcare workers from Quebec (Canada) during the COVID-19 pandemic (n = 416 participants). Associations between their respective components are also tested. A secondary objective is to evaluate if self-compassion is also associated with a set of 38 biomarkers of inflammation (n = 83 participants), potentially associated with the physiological stress response according to the literature. Participants meeting eligibility criteria (e.g.: residing in the province of Quebec, being 18 years of age or older, speaking French, and having been involved in providing care to COVID-19 patients) were recruited online. Participants completed the Occupational Health and Well-being Questionnaire, and some participated in a blood sample collection protocol. Results Results showed significant negative associations between self-compassion, exhaustion, and depersonalization, and a significant positive correlation with professional efficacy. Some self-compassion subscales (mindfulness, self-judgment, isolation, overidentification) were significantly negatively associated with certain biomarkers, even after controlling for confounding variables. Conclusions This study adds to the existing literature by supporting the association of self-compassion with burnout, and reveals associations between self-compassion and physiological biomarkers related to the stress response. Future research directions are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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".