Nurses' perception of readiness for mass casualty events involving children
Bibliographic record
Abstract
After a mass casualty event, there is an expectation that hospitals will safely care for all patients—adults and children alike—who require medical and non-medical attention, regardless of any challenges they may face. In Canada, there are no national emergency preparedness standards for hospitals to follow, and coordination amongst hospital networks across the nation is fragmented at best. Within healthcare, there is an assumption that when resources are limited, patient care that is appropriate for adults is also suitable for children. However, children have unique physical and developmental care needs and they are not simply small versions of their adult counterparts. During mass casualty events where children are involved, hospitals must be ready to receive and provide patient care for both children and adults. However, many studies have shown that due to a lack of funding, resources, and time nurses consistently report feeling unprepared to care for children during mass casualty events. This study focuses on understanding how prepared pediatric-trained nurses are to respond to mass casualty events involving children. Registered Nurse (RN) participants responded to a survey with questions that included four domains: professional demographics and employment history, experience working as an RN in a mass casualty event, knowledge questions related to current organizational mass casualty procedures, and perceptions on professional preparedness. Results of the study found that 74% of participants agree that a mass casualty event involving primarily children, requiring what is known as a Code Orange activation will occur at some point during their career. Nurse participants do not currently receive regular training related to a Code Orange activation and are overall dissatisfied with the little training provided. The study found that the nurses believe emergency preparedness is important to their professional development. The importance of regular training to improve hospital readiness throughout a nurse’s career, beginning in nursing school, is discussed. Other factors that can increase perceived preparedness include years of clinical nursing experience along with experience in specialty services such as emergency care and critical care.
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.
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 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".