Mass casualty incident response: Assessment of the level of preparedness among hospital pharmacists
Bibliographic record
Abstract
Objectives: Mass casualty incidents (MCIs) and outcomes depend on the resources of the admitting institutions and their preparedness, respectively. We assessed the preparedness of hospital pharmacists for MCIs. Materials and Methods: A cross-sectional survey was conducted among 132 pharmacists working in hospitals in Ogun State, Southwestern Nigeria, over 1 month, using a 26-item self-administered questionnaire. Data were analyzed using the Statistical Package for the Social Sciences (SPSS, version 21). A Chi-square test was used for further analysis. P <0.05 was considered statistically significant. Results: The response rate was 79.5% (105/132). Most respondents were 26–30 years, 31.4%, had been practicing for <10 years, 44.8%, and were female, 59.0%. Overall, 42.9% of the respondents had >400 beds, 66 (62.9%), and 48 (45.7%) had general and pharmacy-specific disaster preparedness plans, respectively. Respondents agreed that the hospital committee consensus determined medications to be stocked, 64 (60.9%) and that disaster plans were mainly for natural disasters, 73 (35.4%). Only 7 (6.6%) respondents practiced mock disaster preparedness. There was a significant association between respondents’ year of practice and response on including disaster events in the institutional plan (χ 2 = 95.637, df. = 72, P = 0.033). Most respondents, 95 (90.0%), were positive (mean ± SD: 4.42 ± 0.875) about the need for analgesics during disaster events. Conclusion: Preparation for disaster preparedness was suboptimal based on the number of beds, pharmacy-specific disaster preparedness plan, and practice for mock disasters. This calls for immediate awareness to address these shortfalls through orientation, training, and retraining on preparedness for MCIs.
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How this classification was reachedexpand
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".