Using Functional Neuroimaging to Investigate the Biological Correlates of Burnout and Moral Distress in Healthcare Workers
Bibliographic record
Abstract
Background: The prevalence of burnout, moral distress and depression symptoms among healthcare workers has significantly increased since the onset of the COVID-19 pandemic.Nevertheless, the psychological and biological correlates of burnout subcomponents remain unclear, and no studies to date have examined the biological effects of moral distress.Neuroimaging techniques present a unique opportunity to provide insight into emotional regulation and brain function.Thus, this study utilized resting-state functional magnetic resonance imaging (fMRI) to explore the association between functional connectivity, burnout and moral distress in healthcare workers.Methods: Participants in the current study comprised self-identified licensed healthcare workers employed at Ottawa-area hospitals.All participants completed online selfreport questionnaires and an MRI scan to assess brain function in networks governing emotional regulation.Results: Results from the current study revealed that emotional exhaustion is a stronger predictor of depression and moral distress symptoms in comparison to depersonalization and personal accomplishment.Further, depersonalization was associated with functional connectivity within both the frontolimbic and default mode networks.On the other hand, emotional exhaustion was only associated with functional connectivity within the default mode network in the presence of moderate-to-severe depression symptoms.Finally, moral distress was associated with functional connectivity within the frontolimbic network.Conclusion: These findings highlight the complex impact of burnout subcomponents on mental health symptoms, emphasizing the strong predictive power of emotional exhaustion.Additionally, these results reveal the influence of depersonalization and moral distress on executive function and emotional regulation.Together, this study provides an in-depth examination of the biological underpinnings of burnout and moral distress within a vulnerable population.A. Participants……………………………...………………………...
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".