Assessment of Sick Building Syndrome and Its Associating Factors Among Nurses in the Educational Hospitals of Shahid Sadoughi University of Medical Sciences, Yazd, Iran
Bibliographic record
Abstract
BACKGROUND: Sick Building Syndrome is a diseases associated with indoor air quality accompanied with symptoms such as headache, dizziness, nausea, coughing and sneezing, irritation of eyes, throat and nose mucous membrane, and skin itching and inflammation. The purpose of this study was to investigate the symptoms of the syndrome and its related factors among nurses in teaching hospitals of Shahid Sadoughi University of Medical Sciences, Yazd, Iran. METHODS: The study was conducted amongst nurses of three teaching hospitals of Shahid Sadoughi University of Medical Sciences, in Yazd. In this study the MM040EA questionnaire of sick building syndrome and indoor air quality was used and data extracted from the questionnaires were analyzed using the chi-square test and t-test. RESULTS: The prevalence of sick building syndrome was 86.4%. The prevalence of the syndrome was in no association with age, gender, employment history and type of shift work .The most common symptoms reported by nurses included headache, fatigue and dry hands. Lack of sense of airflow, unpleasant odor in workplace (P<0.05( and also the amount of workload )P<0.001( were in relation with sick building syndrome. CONCLUSION: The high prevalence of sick building syndrome symptoms in the nursing environment was associated with factors of unpleasant odor and high workload of environment. So improvement of environmental conditions such as increasing the efficiency of the HVAC system, increasing fresh air flow in the sector, reducing the nurses workload as well as enhancing the quality of working life, will motivate the nurses and increase productivity in the workplace.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".