Child Abuse and Neglect: Do We know enough? A Cross-sectional Study of Knowledge, Attitude, and Behavior of Dentists regarding Child Abuse and Neglect in Pune, India
Bibliographic record
Abstract
INTRODUCTION: Child abuse and neglect (CAN) is a significant global problem with a serious impact on the victims throughout their lives. Dentists have the unique opportunity to address this problem. However, reporting such cases has become a sensitive issue due to the uncertainty of the diagnosis. The authors are testing the knowledge of the dentists toward CAN and also trying to question the efforts of the educational institutions to improve this knowledge for the better future of the younger generation. MATERIALS AND METHODS: Questionnaire data were distributed to 1,106 members regarding their knowledge, professional responsibilities, and behavior concerning child abuse. RESULTS: There were 762 responses to the questionnaire, yielding a response rate of 68.9%. Although dentists consider themselves able to identify suspicious cases, only a small percentage of the participants correctly identified all signs of abuse and 76.8% knew the indicators of child abuse. Most of them were willing to get involved in detecting a case and about 90% believed that it is their ethical duty to report child abuse. Only 7.2% suspected an abuse case in the past. The numbers indicate a lack of awareness about CAN in these participants. No differences were observed between sexes, year of graduation, types of license, frequency at which children were treated, and formal training already received. CONCLUSION: A large proportion of child physical abuse cases go undocumented and unreported. The data showed that not all dental care providers and students were prepared to fulfill their legal and professional responsibilities in these situations. CLINICAL SIGNIFICANCE: There should be modifications in the dental school curriculum focusing on educational experiences regarding child abuse to strengthen their capability to care and protect children.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".