The Design and Assessment of Mock Mass Disasters for Dental Personnel
Bibliographic record
Abstract
Mass disasters represent a significant challenge for dental personnel who are frequently called upon to provide identifications. Recently-published materials have highlighted the need to prepare such groups for the disaster challenge and to report inadequacies in existing preparation methods with an emphasis on team integration, organization, and the psychological and emotional effects of such work. Many studies have retrospectively reported errors that have been made in disaster situations, but few have addressed the issues proactively. In an effort to provide a prepared team of dental members, a mock disaster exercise (Operation: DENT-ID) is conducted annually in Vancouver, Canada. The present study analyzes the effectiveness of this exercise in relation to team organization, assessment of preparedness, and the emotional and psychological issues. An index of preparedness is developed and described. This index, in the form of a questionnaire, can be given to participants in mock disasters to assess the effectiveness of such exercises. While the focus of this paper is on the assessment of dental personnel, the indices and methods used can be applied to any group working within the disaster team. Results indicate that the increase in preparedness as a result of the exercise was highly significant.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".