Mental health among healthcare workers during COVID-19: a study to oversee the impact of the risk perception and relationship with inflammation from blood-based extracellular vesicles
Bibliographic record
Abstract
Introduction: Preventive measures have been implemented in hospitals during COVID-19, but how these guidelines affected mental health among healthcare workers (HCWs) remains to be determined. On another note, reliable psychological and blood-based markers are needed to promptly identify HCWs at-risk to develop distress. Extracellular vesicles (EVs) originating from brain cross the blood-brain barrier and are detectable in blood, giving them a highly valuable potential for biomarker discovery. In HCWs with or without psychological distress, we investigated how perceived stress during COVID-19 impacted mental health. We then longitudinally evaluated the inflammatory cargo from neuron-, astrocyte-, and microglial-derived EVs that may be associated with psychological distress. Methods: = 15) completed questionnaires for perception of risk, COVID-19-specific posttraumatic symptomatology, psychological distress and burnout, as well as sleep quality. Blood was collected at each visit to characterizing inflammation from brain-derived EVs. Multiple regressions were conducted for all psychological/biological parameters based on the HCWs' final score for psychological distress. Results: Onset of psychological distress was associated early hyperarousal. Moreover, severe distress was associated with increased astrocyte-specific levels of anti-inflammatory interleukin-10 and pro-inflammatory interferon-ɣ. Discussion: Our findings-that need to be replicated in larger studies-suggest that early hyperarousal may be predictive of later onset of psychological distress in HCWs. They also unravel a novel area of biomarker discovery study in psychiatry as inflammation from brain-derived EVs could help targeting "at-risk" individuals.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".