Disaster Health Management: Do Pharmacists Fit in the Team?
Bibliographic record
Abstract
BACKGROUND: In addition to the traditional logistics role, pharmacists are undertaking important new roles in disasters. Despite this, little is known about the level of acceptance of these activities by other providers. PROBLEM: The aim of this study was to determine the international opinion of disaster and health professionals regarding the emerging roles of pharmacists in disasters. METHODS: Delegates at the World Association for Disaster and Emergency Medicine's (WADEM; Madison, Wisconsin USA) 20th Congress in Toronto, Canada (April 2017) were invited to complete an anonymous survey posing eight questions regarding attitudes towards pharmacists' roles in disasters. Quantitative data were analyzed using IBM (IBM Corp.; Armonk, New York USA) SPSS statistical software version 23, and qualitative data were manually coded. RESULTS: Of the 222 surveys handed out, 126 surveys were completed yielding a 56.8% response rate. Of the respondents, 96.8% (122/126) believed pharmacists had a role in disasters additional to logistics. Out of 11 potential roles pharmacists could perform in a disaster, provided on a 5-point Likert scale, eight roles were given a rating of "Agree" or "Strongly Agree" by 72.4% or more of the participants. Lack of understanding of a pharmacist's roles and capabilities was the highest described barrier to pharmacists' roles in disaster management. CONCLUSIONS: This multi-disciplinary disaster health "community" agreed pharmacists have roles in disasters in addition to the established role in supply chain logistics. Participants accepted that pharmacists could possibly undertake numerous clinical roles in a disaster. Several barriers were identified that may be preventing pharmacists from being further included in disaster health management planning and response.WatsonKE, TippettV, SingletonJA, NissenLM. Disaster health management: do pharmacists fit in the team?Prehosp Disaster Med. 2019;34(1):30-37.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".