Anxiety Symptoms and Associated Psychological and Job-Related Factors Among Hospital Nurses
Bibliographic record
Abstract
OBJECTIVE: Recently, burnout and mental health issues regarding nurses are reported increasingly. This study aimed to investigate the prevalence of anxiety symptoms among hospital nurses and determine their association with psychological and job-related factors. METHODS: Data on demographics, job-related characteristics, burnout, Type A behavior patterns, self-esteem, and happiness were collected from 515 nurses working at a university hospital in Korea. Anxiety symptoms were assessed using the anxiety subscale of the Hospital Anxiety and Depression Scale, with scores of 8 or higher indicating the presence of anxiety symptoms. Demographic, job-related, and psychological factors were compared according to the presence of anxiety. Logistic regression was conducted to identify factors associated with anxiety symptoms. RESULTS: Two hundred and four (39.6%) participants had anxiety symptoms. Self-esteem and happiness were associated with a lower risk of anxiety symptoms, whereas burnout was associated with a higher risk of anxiety symptoms. Furthermore, being female, having a career of less than five years, and requiring counseling due to stress were associated with a higher risk of anxiety symptoms. Being younger, female, or a basic nurse; having a career of less than five years; partaking in shift work; experiencing job dissatisfaction; requiring counseling due to stress; being exposed to higher levels of burnout; and having lower levels of self-esteem and happiness were all found to be significantly correlated with anxiety symptoms. CONCLUSION: These findings suggest that promoting self-esteem and happiness while reducing burnout may be beneficial in preventing and managing anxiety symptoms among hospital nurses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".