A Survey on the Relationship between Dentists’ Workplace conditions and Their Quality of Life in 2014
Bibliographic record
Abstract
Introduction: The prevalence of job-related stress has been proved to be high within the dentists in different studies. This stress, resulted from such factors as poor lightening as well as noise of dental office, can cause emotional distress, threaten dentists’ physical health and affect their quality of life. Hence, this study aimed to evaluate dentists’ professional quality of life, job-related stress and two important workplace factors of lighting and noise.\n\nMethods: In this analytical-descriptive and cross sectional study, the researcher visited the dental offices in Shiraz and measured lighting and noise of the places. Moreover, dentist's quality of life and job stress were determined using McGill quality of life questionnaire and job-stress questionnaire. The relationship between quantitative variables was determined using regression test and the multiple regression t est was also applied for the modeling process.\n\nResults: The local noise mean cased by the dental drills was 75.5 and 74.5 in the public and private offices, respectively. In 2.2% of the dental offices, lightening condition was reported below the standard levels. The study results revealed that 58.9% of dentists participating in this study experienced good or fairly good quality of life.\n\nConclusion: The study findings suggested that workplace conditions were correlated with the dentists’ professional stress and quality of life. Training how to manage this psychological disorder can significantly reduce its destructive effects and as a result, quality of life can be increased.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".