ASSESSMENT OF DENTISTS' SELF-REPORTED LEVELS OF INCOME AND BURNOUT DIMENSIONS: A PRELIMINARY INVESTIGATION
Bibliographic record
Abstract
The aim of this preliminary investigation was to assess dental professionals’ income satisfaction and its related levels of burnout dimensions: EE, DP, and PA. An anonymous descriptive cross-sectional survey was held among 156 dentists working in the capital of Republic of Bulgaria, Sofia. The questionnaire consisted of demographic data and the Maslach Burnout Inventory-Human Services Survey (MBI-HSS) sections. A total of 136 dentists provided duly completed questionnaires (RR = 90.7%), of which 56 (41.2%) were males. Surprisingly, almost a quarter – 32 (23.5%), reported having unsatisfactory income from dentistry contrary to the notion that this occupation was considered well paid and providing security in this regard. This group of dentists demonstrated higher levels of burnout than those with good or excellent income. They exhibited higher scores mainly in EE and reduced PA subscales. Therefore, recommendations for positive outcomes of dental procedures which are crucial for patient well-being and satisfaction, might have a key role in evaluation of achieved results, including their financial aspect. Ultimately, better compensation, motivation and performance could effectively help reduce the risk of burnout.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".