Approach to Mental Disorders in Dental Education in a Brazilian State
Bibliographic record
Abstract
Oral diseases present higher prevalence in people with mental disorders, which makes dental care a possible means for screening and referral to mental health specialized care. Because some dental conditions can be caused by or associated with mental disorders, dentists can identify patients’ mental health care needs. However, they have shown low confidence and a lack of training in dealing with mental health patients, which may indicate a deficiency in the approach to mental disorders in dental education. This study aimed to verify whether undergraduate Dentistry courses in a Brazilian state introduce knowledge about adult mental disorders into their curriculum content. The courses operating in the state of Minas Gerais, Brazil, were assessed by information available at the e-MEC system, the official database of courses and Higher Education Institutions in Brazil, and at institutions’ websites. Two researchers independently assessed pedagogical projects, curriculum matrices, syllabi, and teaching plans. Psychology disciplines Applied Psychology and Psychology-related were identified, and their syllabi and teaching plans were screened, searching for themes related to mental disorders and/or psychopathology. Of the 73 courses included, 34 were evaluated, and only five courses presented disciplines with some approach to mental disorders and/or psychopathology. The results showed that a large part of undergraduate Dentistry courses assessed do not address knowledge about adult mental disorders as part of dental education. Considering the impact of mental disorders on oral and systemic health, it is important to reflect on the role of dentists in mental health care and on the responsibility of Higher Education Institutions regarding the development of skills and competencies of these professionals. The results of this study draw attention to the need to address adult mental disorders in dental education, reinforcing mental health as an important aspect of patients’ general health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".