Assessment of a Novel Module for Training Dental Students in Child Abuse Recognition and Reporting
Bibliographic record
Abstract
This study assessed the merits of introducing a novel, online interactive training module designed to positively engage dental students and teach them to recognize and report signs of child abuse and neglect. The study aimed to determine if the online training module educated the students equivalently or better than a lecture presentation of the same content. Seventy-two students from Columbia University College of Dental Medicine's class of 2015 (90 percent of the class) agreed to participate and were randomly assigned to either a traditional lecture-based presentation or the online training module. Study participants were given a twenty-question multiple-choice pretest on their knowledge of child abuse recognition and reporting prior to the start of the study. The same instrument was administered as a posttest. At the end of the training, questionnaires were also given to both groups to assess students' perceptions of the two educational methodologies. The results showed that the interactive online training module was more effective than the lecture-based method. Results of the posttest comparison of the two groups were statistically significant (p<0.05) in favor of the online training group. Additionally, the students reported that the interactive online training module was engaging and a helpful resource, but on average they did not prefer it as a total replacement for the lecture-based approach.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".