Defining Roles for Pharmacy Personnel in Disaster Response and Emergency Preparedness
Bibliographic record
Abstract
Ongoing provision of pharmaceuticals and medical supplies is of key importance during and following a disaster or other emergency event. An effectively coordinated response involving locally available pharmacy personnel-drawing upon the efforts of licensed pharmacists and unlicensed support staff-can help to mitigate harms and alleviate hardship in a community after emergency events. However, pharmacists and their counterparts generally receive limited training in disaster medicine and emergency preparedness as part of their initial qualifications, even in countries with well-developed professional education programs. Pharmacy efforts have also traditionally focused on medical supply activities, more so than on general emergency preparedness. To facilitate future work between pharmacy personnel on an international level, our team undertook an extensive review of the published literature describing pharmacists' experiences in responding to or preparing for both natural and manmade disasters. In addition to identifying key activities that must be performed, we have developed a classification scheme for pharmacy personnel. We believe that this framework will enable pharmacy personnel working in diverse practice settings to identify and undertake essential actions that are necessary to ensure an effective emergency response and will promote better collaboration between pharmacy team members during actual disaster situations. (Disaster Med Public Health Preparedness. 2017;11:496-504).
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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 | 0.025 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".