Severe Acute Respiratory Syndrome (SARS): The Pharmacist's Role
Bibliographic record
Abstract
OBJECTIVES: After two outbreaks of severe acute respiratory syndrome (SARS) occurred in Toronto, Ontario, Canada, from March-June 2003, we reviewed the unexpected role and responsibilities of pharmacists during these two crises, and present strategies for better crisis preparedness. METHODS AND RESULTS: Pharmacists were actively involved in battling the SARS crises. After conducting extensive literature searches and evaluations, pharmacists prepared administration and dosing guidelines for the two investigational drugs, ribavirin and interferon alfacon-1, that were being used to treat the syndrome. They provided direct patient care under modified conditions. They revised drug distribution procedures and developed new ones to meet more stringent infection-control standards. Collaborative teamwork with key stakeholders was important in accomplishing tasks in an efficient and timely manner. Regular communication with health care staff took place internally and externally. Education and updated information for pharmacists was crucial. CONCLUSION: Pharmacists can play a vital role during crises in the areas of drug distribution, drug information, and direct patient care. Collaborative teamwork and close communication are keys to success. Pharmacists must be proactive and take a leadership role in assuming pharmacy-related responsibilities. By evaluating what worked and what didn't, pharmacists can develop procedures for future crises requiring pharmacy support.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".