Bibliographic record
Abstract
Background: There has been growing concern about possible drug shortages in Canada, yet we know little about the frequency and impact of shortages in community pharmacies. Objective: The objective of this survey was to determine the extent of any drug shortages in Canadian community pharmacies and the implications of these shortages for community pharmacists. Methods: Surveys and log sheets were faxed to a random sample of 1000 Canadian community pharmacists. Results: More than half of the pharmacists experienced a shortage during one shift, and 80% experienced drug shortages over a one-week period. The three main reasons given for shortages were manufacturing problems, cross-border drug trade, and raw material shortages. The drug products that were most frequently in short supply were Chronovera, Loestrin, Sodium Sulamyd eye drops, and Minestrin. Pharmacists estimated that they spent an average of 17.5 minutes each shift dealing with drug shortages. Conclusion: Most pharmacists are experiencing shortages and feel that these shortages have become more frequent over the past year. There are a variety of factors that contribute to drug shortages and, while the amount of time spent dealing with them is manageable at the present time, increases in shortages will further stress the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".