Sources of Stress and Coping Strategies among Iranian Physicians
Bibliographic record
Abstract
BACKGROUND: Physicians are at risk of having high levels of stress which affect their performance. Finding the stressors and the coping skills to manage stress could be used to develop program to decrease stressful situation. No study has been done on Physicians' stress and coping in Iran. The main objective of this study is to find out the main stressors and coping strategies among Iranian Physicians working in hospital in Tehran-Iran.METHODS: A cross-sectional study was performed on 780 Physicians,using a questionnaire consisted of two sections ;The first section were the stressors which included 67 questions and The second section were The Brief COPE with 28-items for assessing a broad range of coping behaviors among respondents.RESULTS: A total of 1100 questionnaires were distributed to all the available Physicians in the hospitals selected. 780 Physicians returned complete questionnaires with observed response rate of 75%. The majority of respondents (56.9%) were women. The first 3 sources of stress in workplace (Job stressors) are physical environment problem (75%), too much volume of work and poorly paid. The main sources of stress outside the work place (non-job stressors) ranked by Physicians were; financial problem (9.09), not enough time to spend with family (8.87), conflicts with household tasks (7.36).The top five coping strategies used by Iranian Physicians were Behavioral Disengagement, Planning, Instrumental support, Acceptance, and turning to religion.CONCLUSIONS: This study revealed that both workplace and non-job sources of stress can affect the Physicians performance and there is an association between gender and coping skills.
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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 | 0.001 | 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".