How do community pharmacies in Ontario manage drug shortage problems? Results of an exploratory qualitative study
Bibliographic record
Abstract
Background: Pharmacists report spending a considerable amount of time dealing with drug shortages. There is no research in Canada identifying and describing the strategies and resources that pharmacists use to minimize disruption and continuity of care for patients. Methods: An exploratory qualitative methodology was used. Community pharmacists and technicians in Ontario were interviewed using a semi-structured protocol. Verbatim transcripts were generated and coded by at least 2 independent reviewers using content analysis methods to identify management strategies. Results and Discussion: A total of 14 pharmacists and 7 regulated pharmacy technicians participated in this study. The following 5 main strategies for managing drug shortages were identified: (1) using the supplier, (2) generic options, (3) brand options, (4) contacting other pharmacies and (5) switching to a different medication. Conclusion: The strategies identified through this research can provide pharmacists with some guidance in approaching the real-world problem of drug shortages. It also highlights opportunities for organizations, government and manufacturers to provide additional support for pharmacists to minimize disruptions for patients and to ensure current ad hoc practices do not further compound shortage issues. Can Pharm J (Ott) 2020;153:xx-xx.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".