A systematic review and meta-analysis of failure to take history as a barrier of reporting child abuse by dentists in private and state clinics
Bibliographic record
Abstract
BACKGROUND: Since the symptoms of child abuse and neglect often manifest in the orofacial region, the dental team has a key role in identifying children subjected to abuse. This study was aimed to explore the prevalence of failure to take history as a barrier to reporting child abuse by dentist around the world. MATERIALS AND METHODS: In this systematic review and meta-analysis, PubMed, Embase, Scopus, Google Scholar, ProQuest, Cochrane, and ISI databases were searched for the cross-sectional articles in English languages on barriers to reporting child abuse and lack of knowledge about referral procedures by dentists since 1985 using Medical Subject Headings (MeSH). The full-texts of all included articles were obtained and assessed for quality according to Newcastle-Ottawa Scale adapted for cross-sectional studies. The qualified articles were then studied thoroughly and results were extracted. Data were analyzed by Comprehensive Meta-Analysis software using meta-analysis and random effects model. Heterogeneity was determined by Q-test and I-square index. RESULTS: A total of 17 articles were included in the meta-analysis. The prevalence of lack of knowledge about referral procedures as a barrier was determined according to the meta-analysis of the number of relevant articles and was (55%, confidence interval: 0.48, 0.62). CONCLUSION: The analysis of various studies revealed lack of information about referral procedures as an important barrier to report child abuse by dentists.
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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 | 0.022 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".