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 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 | 0.002 |
| 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 | 0.001 |
| 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".