Religion and the Mediating Role of Alexithymia in the Mental Distress of Healthcare Workers During the Coronavirus Disease 2019 Pandemic in a Psychiatric Hospital in China
Bibliographic record
Abstract
The outbreak of the coronavirus disease 2019 (COVID-19) has created unprecedented challenges to the healthcare system, religion, and alexithymic trait that impacts the psychological resilience of healthcare workers during the COVID-19 pandemic. This study aimed to investigate the role religion and alexithymia play in mental distress and the level of happiness of psychiatric hospital healthcare workers in China amidst the COVID-19 pandemic. Furthermore, whether symptom dimensions (anxiety, depression, hostility, inferiority, and insomnia) are associated with the level of happiness, and a 6-month follow-up was also investigated. A total of one-hundred and ninety healthcare workers were recruited from a psychiatric hospital in Jilin, China, and 122 were followed up after 6 months. All participants filled out the 20-item Toronto Alexithymia Scale, five-item Brief-Symptom Rating Scale, and the Chinese Oxford Happiness Questionnaire. The mental distress of healthcare workers decreased from 2.6 to 1.5% in 6-months. Religious belief was not associated with the mental distress or happiness of healthcare workers. Instead, for those whose anxiety decreased over 6 months, their social adaptation status increased. For those whose inferiority level decreased over time, their perceived level of psychological well-being and overall happiness increased. In over half a century of living in different societies, religion stabilizes the mental health of those in Taiwan amidst the stress of the COVID-19 pandemic, but not in China. However, both regions found healthcare workers with alexithymic traits experienced a higher level of mental distress, implying that the collectivist culture of Confucian philosophy continues to influence the emotional expression and alexithymic traits of healthcare workers in China and Taiwan. To ensure a healthy and robust clinical workforce in the treatment and control of the pandemic, the cultural impact on the psychological resilience of medical workers needs to be addressed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".