Knowledge, dentist confidence and management of periodontal patients among general dentists from Belarus, Lithuania, Macedonia, Moldova and Romania
Bibliographic record
Abstract
BACKGROUND: Evidence concerning periodontal practice in Eastern European countries is scarce. The aim of the present study was to investigate periodontal risk knowledge, patient management and self-perceived confidence among General Dentists (GDs) from five Eastern European regarding their provision of periodontal care. METHODS: GDs from Belarus, Lithuania, Macedonia, Moldova and Romania participated in a questionnaire survey. Power calculations were used to identify the sample size for each country. The structured questionnaire included several domains of inquiry. The socio-demographic domain inquired about dentist's age, gender and years of clinical experience. The dental practice domain inquired about practice location, practising or not practising in a group practice and having or not having a periodontist or a dental hygienist in the practice. The distributions of answers across-countries were compared employing one way ANOVA (comparison of means) or Chi square test (comparison of proportions). For each country, the predictors of the study outcomes: a summative knowledge score for periodontal risks and dentist's confidence level were identified employing either linear or logistic multiple regression models. RESULTS: The sample comprised 390 Belarussian, 488 Lithuanian, 349 Macedonian, 316 Moldovan, and 401 Romanian GDs. The majority of GDs (~ 80%) practiced in urban areas. Age and gender distributions differed significantly among countries. Significant across-country differences were found regarding working/not working in a group practice, having/not having access to a periodontist/dental hygienist and in proportions of patients receiving periodontal treatments or being referred to specialists. None of Macedonian patients nor the majority of Moldovan patients (78%) were referred to periodontists. There were also significant across-country differences in diagnosis, patient management and periodontal knowledge. Only in the Lithuanian cohort were dentists' confidence levels associated significantly with their knowledge. In all countries, taking a medical history was a consistent and significant predictor of having higher periodontal knowledge score. Except in Belarus, periodontal risk assessment was a significantly consistent predictor of certainty levels associated with the provision of periodontal treatments. CONCLUSIONS: There were substantial differences among GDs in the five countries regarding diagnosis, dentist's confidence and management of periodontal patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".