A study of depression and anxiety among doctors working in emergency units in Denizli, Turkey
Bibliographic record
Abstract
BACKGROUND: Major work has been carried out on the psychological well-being of emergency room doctors in the US, Canada and in other developed countries, but little has been published regarding the same in the countries in economic transition. OBJECTIVE: To determine the level of, and the factors related to, depression and anxiety among doctors working in emergency units in Denizli, Turkey. METHODS: This cross-sectional study was conducted in May 2004, using a sample of 192 doctors employed in emergency units in Pamukkale University Hospital, the City Hospital, the Social Security Hospital, private hospitals, citywide primary healthcare centres and 112 emergency services in Denizli, Turkey. Data were obtained using a self-administered questionnaire, including questions on sociodemographic characteristics and two instruments determining the level of depressive symptoms and anxiety. Logistic regression was the method chosen for multivariate statistical analysis. RESULTS: The mean (standard deviation (SD)) depression score was 10.6 (6.5) and the frequency (%) of depression was 29 (15.1). Not having any hobby and having high anxiety scores were salient factors among doctors experiencing depressive symptomatology in bivariate comparisons. Logistic regression analysis showed that not having any hobby (p = 0.07) and having increased anxiety scores (p<0.001) were positive contributors to depression scores. The mean (SD) anxiety score was 8.7 (8.2) and the frequency (%) of anxiety was 28 (14.6). Being a woman, having a low monthly income and having high depression scores contributed considerably to the anxiety of doctors in bivariate comparisons. Low monthly income (<1000 v 1000-2000 YTL) (p = 0.03), the number of years spent in emergency units (p = 0.03) and having high depression scores (p<0.001) were the factors that contributed significantly to the anxiety of doctors in the multivariate regression analysis. CONCLUSION: The considerable amount of depression and anxiety found among doctors in this study should trigger further work. Studies using more powerful designs would help to illuminate the factors leading to depression and anxiety, which result in attrition among doctors from emergency units.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".