The need for an emergency planning and preparedness strategic plan for pharmacy leadership
Bibliographic record
Abstract
There is nothing new about disasters and emergencies impacting our society. The 2022 ASHP statement on the role of the pharmacy workforce in emergency preparedness stated, “Healthcare systems must continue to engage their employees, leaders, and communities to prepare for these unforeseen events to alleviate extended disruption of services for their patients in a time of need.”1 But what does this mean for pharmacy leadership? The path to a consistent, proactive process in minimizing the impact of emergency situations begins with pharmacy leadership teams. We need to build strong pharmacy leadership teams that are prepared to respond and not simply react to the situation presented in front of them. From this strong foundation, we can lead our pharmacy workforce through the planning and responding to a health emergency and beyond. Additionally, it is not up to others to dictate the pharmacy emergency planning and preparedness strategic plan. However, others will step in and tell us how pharmacy should respond in the absence of us having detailed plans and routinely undertaking health emergency planning and preparedness activities.2 Below are some steps to consider when building the road map for an emergency planning and preparedness strategic plan.
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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 | 0.032 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.018 | 0.042 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".