Hospital response for children as a vulnerable population in radiological/nuclear incidents
Bibliographic record
Abstract
Emergency planning in the healthcare industry is a relatively new field. Planning for radiological and nuclear (R/N) events is even newer. Consequently, the amount of training and education that has been produced and conducted, thus far, has been minimal when compared with other fields in healthcare. To compound this issue, the planning and research that have been completed has, without a doubt, been focused on adults. Children represent a significant portion of the population. They would, unquestionably, be affected by any large-scale R/N event and have been, for the most part, ignored in planning processes. Identified gaps in the planning and preparedness process should provide the basis for moving forward and putting protocols in place for dealing with children during these types of incidents. Gaps can be summarised into categories, or stages of reaction to an R/N event. These categories include: mitigation and planning processes; triage or incident response issues and recovery procedures following the initial response. The primary goals of the hospital in a hazardous event are to: In order to achieve the above goals, hospitals must have procedural response plans, personal protective equipment (PPE), decontamination equipment and training to ensure knowledge and implementation for an effective response. Figure 1 provides a framework for hospital decontamination planning.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".