Oral Anti-Cancer Agents in the Community Setting: A Survey of Pharmacists in Newfoundland and Labrador
Bibliographic record
Abstract
Background: Over the past decade, there has been a sharp rise in the approval of orally administered anti-cancer agents for disease control. The increase in the use of oral anti-cancer agents (OAAs) raises concerns that community pharmacists may not have the training to safely dispense these agents and provide effective patient care. In order to identify the needs of community pharmacists with respect to oral anti-cancer therapy, a survey was conducted in the province of Newfoundland and Labrador. Methods: A structured electronic mailing strategy was used. Standardized data collection forms with a cover letter were electronically mailed to 560 practising pharmacists. Survey items included questions related to demographic information, practice setting, current knowledge related to cancer therapy, education needs, access to resources, patient education, patient and pharmacist safety and required elements of an OAA prescription. Results: The response rate was 39%. Only 9.6% of respondents felt that they had received adequate oncology education at the undergraduate level and approximately 31% had attended a continuing education event related to oncology in the past 2 years. Just 17% of respondents stated that they used protective equipment when dispensing OAAs. Only 28% of the pharmacists who responded were familiar with the common doses of OAAs and approximately 25% felt comfortable educating patients on these medications. Conclusions: A substantial portion of community pharmacists in Newfoundland and Labrador do not have a solid understanding of oral anti-cancer therapy. These educational gaps must be addressed to ensure patient safety as well as the safe handling and dispensing of OAAs by community pharmacists.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".