Knowledge, Attitudes, and Experiences of a Group of Turkish Dentists regarding Child Abuse
Bibliographic record
Abstract
Child abuse is a universal problem with critical lifelong effects. This study aimed to evaluate knowledge, attitudes, experiences of dentists regarding child abuse and to increase relevant awareness of them. A self-administered questionnaire with 48 questions about dentists’ personal and educational information and their level of knowledge, attitudes, experiences on child abuse was implemented. Out of targeted 305 participants, 183 (60.0%) returned completely filled out questionnaires. Majority of participants knew signs and symptoms of child abuse. Of the participants, more than half were well-informed on what to do, however, one quarter had no knowledge about where to report in case of child abuse. In identifying child abuse, 39.3% of the participants found themselves inadequate. The most commonly stated reason for low rate of reporting was hesitancy to identify the case as abuse, and all participants needed more training. Only 12 (6.6%) participants suspected a case of child abuse. Dentists who had children and were long-time experienced and generalist did not have sufficient knowledge about legal obligations, signs and symptoms of child abuse (p<0.05). Arrangements and training programs to increase knowledge, awareness, and responsibility levels of dentists about child abuse appear to be a critically important topic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".