The role of dental professionals in identifying, reporting, and supporting domestic violence victims
Bibliographic record
Abstract
Domestic violence is a pervasive social issue affecting individuals across all demographics and has severe consequences for both the victims and society. Domestic violence is commonly defined as the exertion of power by one individual over another within a relationship, aiming to establish a sense of fear, control, and authority. The connection between domestic violence and oral health is established, with common oral health issues associated with domestic violence, such as dental trauma, head and neck bruises and injuries as well as facial fractures. Dental professionals play a crucial role in detecting signs of domestic violence by closely examining the head and neck region and the oral cavity during routine examinations. The significance of approaching patients suspected of experiencing domestic violence with sensitivity and empathy is of utmost importance. Recommendations include establishing trust, maintaining confidentiality, using open-ended questions, and providing information about local resources. Legal and ethical considerations are paramount, highlighting the obligations of dental professionals in cases of suspected domestic violence, including mandatory reporting laws and the balance between patient autonomy and safety. Challenges faced by dental professionals in reporting and intervening are discussed as well in this narrative review, emphasizing the importance of collaboration with other healthcare professionals and support services. This review underscores the vital role of dental care providers in recognizing signs of domestic violence, promoting intervention and support, and contributing to the well-being and safety of individuals impacted by domestic violence.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".