Competence and Training Needs in Infectious Disease Emergency Response Among Chinese Nurses: Cross-Sectional Study
Bibliographic record
Abstract
Background: In recent years, the frequent outbreaks of infectious diseases and insufficient emergency response capabilities, particularly issues exposed during the COVID-19 pandemic, have underscored the critical role of nurses in addressing public health crises. It is currently necessary to investigate the emergency preparedness of nursing personnel following the COVID-19 pandemic completely liberalized, aiming to identify weaknesses and optimize response strategies. Objective: This study aimed to assess the emergency response competence of nurses, identify their specific training needs, and explore the various elements that impact their emergency response competence. Methods: Using a multistage stratified sampling method, 5 provinces from different geographical locations nationwide were initially randomly selected using random number tables. Subsequently, within each province, 2 tertiary hospitals, 4 secondary hospitals, and 10 primary hospitals were randomly selected for the survey. The random selection and stratification of the hospitals took into account various aspects such as geographical locations, different levels, scale, and number of nurses. This study involved 80 hospitals (including 10 tertiary hospitals, 20 secondary hospitals, and 50 primary hospitals), where nurses from different departments, specialties, and age groups anonymously completed a questionnaire on infectious disease emergency response capabilities. Results: This study involved 2055 participants representing various health care institutions. The nurses' mean score in infectious disease emergency response competence was 141.75 (SD 20.09), indicating a moderate to above-average level. Nearly one-fifth (n=397, 19.32%) of nurses have experience in responding to infectious disease emergencies; however, they acknowledge a lack of insufficient drills (n=615,29.93%) and training (n=502,24.43%). Notably, 1874 (91.19%) nurses expressed a willingness to undergo further training. Multiple linear regression analysis indicated that significant factors affecting infectious disease emergency response competence included the highest degree, frequency of drills and training, and the willingness to undertake further training (B=-11.455, 7.344, 11.639, 14.432, 10.255, 7.364, and -11.216; all P<.05). Notably, a higher frequency of participation in drills and training sessions correlated with better outcomes (P<.001 or P<.05). Nurses holding a master degree or higher demonstrated significantly lower competence scores in responding to infectious diseases compared with nurses with a diploma or associate degree (P=.001). Approximately 1644 (80%) of the nurses preferred training lasting from 3 days to 1 week, with scenario simulations and emergency drills considered the most popular training methods. Conclusions: These findings highlight the potential and need for nurses with infectious disease emergency response competence. Frequent drills and training will significantly enhance response competence; however, a lack of practical experience in higher education may have a negative impact on emergency performance. The study emphasizes the critical need for personalized training to boost nurses' abilities, especially through short-term, intensive methods and simulation drills. Further training and tailored plans are essential to improve nurses' overall proficiency and ensure effective responses to infectious disease emergencies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".