Longitudinal Insights into the Mental Health of Healthcare Workers: Emotional Shifts During Two Years of the COVID-19 Crisis
Bibliographic record
Abstract
OBJECTIVE: Numerous studies have highlighted the prevalence of mental health disorders among healthcare professionals during the COVID-19 pandemic, with varying indications of emotional strain. This study compares the psychological functioning of healthcare workers at the onset of the pandemic and two years later, offering a comprehensive assessment of their emotional and mental health status in the evolving context of COVID-19. METHODS: = 252). The study employed the Toronto Alexithymia Scale-20 (TAS-20), Cohen's Perceived Stress Scale (PSS-10), Mini-COPE, Acceptance of Illness Scale (AIS), Emotional Processing Scale (EPS), STAI, and PANAS to assess psychological responses and coping mechanisms. RESULTS: Findings revealed a significant increase in denial, substance use, self-blame, negative mood, and impaired psychological and somatic functioning, alongside heightened symptoms of depression and anxiety. Conversely, a marked decrease in planning, positive reinterpretation, acceptance, religious coping, and seeking social support (both emotional and instrumental) was observed over the two-year period. CONCLUSION: The prolonged nature of the COVID-19 pandemic has profoundly affected the psychological resilience of healthcare professionals, eroding critical emotional resources necessary for maintaining interpersonal relationships and mental well-being. These results underscore the need for targeted interventions to support the mental health of medical staff in the ongoing crisis.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".