Are community pharmacists equipped to ensure the safe use of oral anticancer therapy in the community setting? Results of a cross-country survey of community pharmacists in Canada
Bibliographic record
Abstract
PURPOSE: Oral anticancer agents offer significant benefits over parenteral anticancer therapy in terms of patient convenience and reduced intrusiveness. Oral anticancer agents give many cancer patients freedom from numerous hospital visits, allowing them to obtain their medications from their local community pharmacy. However, a major concern with increased use of oral anticancer agents is shift of responsibility in ensuring the proper use of anticancer agents from the hospital/clinical oncology team to the patient/caregiver and other healthcare providers such as the community pharmacists who may not be appropriately trained for this. This study assessed the readiness of community pharmacists across Canada to play this increased role with respect to oral anticancer agents. METHODS: Using a structured electronic mailing strategy, a standardized survey was mailed to practicing pharmacists in five provinces where community pharmacists were dispensing the majority of oral anticancer agents. In addition to collecting basic demographic and their practice setting, the survey assessed the pharmacists' knowledge regarding cancer therapy and oral anticancer agents in particular, their education needs and access to resources on oral anticancer agents, the quality of prescriptions for oral anticancer agents received by them in terms of the required elements, their role in patient education, and steps to enhance patient and personal safety. RESULTS: There were 352 responses to the survey. Only 13.6% of respondents felt that they had received adequate oncology education at the undergraduate level and approximately 19% had attended a continuing education event related to oncology in the past 2 years. Only 24% of the pharmacists who responded were familiar with the common doses of oral anticancer agents and only 9% felt comfortable educating patients on these medications. CONCLUSIONS: A substantial portion of community pharmacists in Canada lack a solid understanding of oral anticancer agents and thus are poorly equipped to play a major role in ensuring their appropriate use. More education and training on oral anticancer agents are urgently required.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".