Effect of Workplace Noise and Light Conditions on Dentists' Quality of Life, Shiraz, 2013
Bibliographic record
Abstract
Received: April 2014, Accepted: September 2014 Background: It has been prover that the prevalence of experiencing job related stress is very high among the dentists. This stress can be the result of factors such as poor lightening of dental office and noise. This stress can cause emotional distress and threaten dentists’ physical health and affect the quality of their life. This study is conducted to determine dentists’ professional quality of life and job-related stress and two important workplace factors that can affect them which are lighting and noise. Materials and Methods: In this cross-sectional study the researchers visited the dental offices in Shiraz city and measured lighting and noise of the places, also dentist's quality of life and job stress were determined using McGill quality of life questionnaire and job-stress questionnaire. Results: The relationship between quantitative variables was determined by using regression test and the multiple regression test was used for the modeling process. The average of local noise caused by dental drills and other parameters was 75.5 and 74.5 in public and private offices. In 2.2% of dental offices lightening condition was below the standard levels. Results show that 58.9% of dentists participating in this study experience good or fairly good quality of life. Conclusion: The results suggest that workplace environmental situation and dentists’ professional stress and quality of life are correlated. So adjusting effective workplace parameters to the standard levels can lead to increase in the dentists’ quality of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".