Pharmacists’ perceptions and attitudes toward drug importation into the State of Florida
Bibliographic record
Abstract
BACKGROUND: The Department of Health and Human Services and the Food and Drug Administration released the Safe Importation Action Plan in July 2020 detailing methods to import medicines from Canada to combat increasing drug costs. In November 2020, Florida became the first state in the United States to create and propose an importation plan from Canada. This study examines the proposal submitted by Florida, Florida pharmacists' perceptions of the program on patient safety, and Florida pharmacists' thoughts on the pharmacy operational impact. METHODS: This was a cross-sectional study utilizing an electronic questionnaire sent to pharmacist members of the Florida Pharmacy Association. The survey incorporated closed-ended and open-ended questions. The results from the study were reported and analyzed through descriptive statistics, qualitative and quantitative data. RESULTS: Two-hundred and forty-four pharmacists responded to the survey. Of those respondents, 25% stated they had no knowledge about Florida's drug importation plan. Less than 12% of respondents stated they would trust the safety and quality of imported medicines. Seventy percent of pharmacists expressed concerns regarding the changes required in pharmacy operations to increase medicine safety. About half of the respondents questioned whether this plan would promote cost-savings as intended. CONCLUSION: Florida pharmacists believe the drug importation plan does not address all aspects of patient and medicine safety and expressed concerns regarding logistical operations of a pharmacy. This article highlights those concerns and acts as a summons to action.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".