Dentist Job Satisfaction: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Because of the heterogeneous nature of the evidence regarding dentists' job satisfaction, an overview was necessary to examine dentists' level of job satisfaction and to determine related work environmental factors. MATERIALS AND METHODS: A literature search was conducted using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Electronic database searches of PubMed/MEDLINE, EMBASE, and Web of Science were performed until March 1, 2020. Two independent authors collected data and assessed the methodological quality of primary studies using the Newcastle Ottawa Scale. RESULTS: Nine studies were included from the 1987 initially retrieved. Among the included studies, 5 exhibited a neutral level of satisfaction and originated from China, South Korea, Egypt, and the United States, and 3 studies from Canada, Lithuania, and the United States showed a high level of satisfaction. Only 1 study did not report the mean job satisfaction score. According to bias evaluation, 9 studies were considered low risk. CONCLUSION: The findings showed that dentists were satisfied with their jobs at a moderate to high level, and specialists were more satisfied than general dentists. Regarding work environmental factors, the 6 most satisfied factors were patient relationships, respect, delivery of care, staff, professional relationship, and professional environment. Five of the least satisfied factors were personal time, stress, income, practice management, and professional time. However, longitudinal studies would be required to identify changes in these factors. Further studies should be performed in middle- and low-income countries using the Dentist Satisfaction Survey, including stress evaluation.
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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".