The Design and Assessment of Mock Mass Disasters for Dental Personnel
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it