Creating a Disaster Ready Pharmacy Workforce: Evaluation of a Disaster Tabletop Exercise
Bibliographic record
Abstract
Introduction: While the importance of pharmacists' involvement in disaster management is becoming increasingly recognized in the literature, there are few mechanisms by which pharmacists can prepare themselves for emergencies. This project aimed to determine the effectiveness of a disaster tabletop exercise (TTX) in preparing pharmacy staff for disasters. Method: A TTX was held at the American Society of Health-System Pharmacists Summer Meeting which was held in Phoenix, Arizona in June 2022. The workshop incorporated an evolving emergency scenario in which participants worked through activities pertaining to the mitigation, preparedness, response, and recovery cycle. The scenario involved a hypothetical storm and landside scenario across fictional towns in Arizona, US. Workshop attendees worked in small groups on one of two provided hospital profiles. The attendees were invited to complete a pre-post survey assessing their perceptions of disaster management including perceived preparedness. This survey was previously developed, piloted, and published. The paper surveys were collected at the end of the workshop and inputted into RedCap. Data were descriptively summarized using SPSS, and pre-post survey results were compared using appropriate statistical tests. Results: The workshop was attended by 40 pharmacy personnel and 31 completed the survey. All participants agreed that the exercise was well structured, realistic, allowed them to test their response plans and systems, and helped improve their understanding of their role and function in disaster response. After the workshop, participants' perceptions of their ability to prevent, respond, and recover from a disaster all significantly improved (p=0.004, 0.013, and 0.013 respectively). However, perceptions of their preparedness for a disaster did not significantly change (p=0.197). Conclusion: This study adds to the evidence of the effectiveness in training and preparing the pharmacy workforce. The TTX improved the understanding and perceived capabilities of pharmacy personnel in responding and recovering from 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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".