Optimizing patient education of oncology medications: A descriptive survey of pharmacist-provided patient education in Canada
Bibliographic record
Abstract
BACKGROUND: The incidence of cancer is increasing in Canada due to an aging and growing population. This frequently necessitates chemotherapy, which is a high-risk treatment, often given as a part of a complex regimen with serious side effects. A review of the evidence of pharmacy-provided patient education initiatives targeted to oncology patients revealed that minimal is known about this service. OBJECTIVE: The objective of this study was to determine the different models of patient education of oncology medications delivered by pharmacists to adult oncology patients in a hospital or cancer center in Canada. METHODS: The study design was a descriptive online survey developed by the investigation team and was distributed to pharmacists who provided patient education to adult oncology patients. The primary outcome of this research project was to describe self-reported pharmacist-provided patient education of oncology medications across Canada. The survey data was analyzed quantitatively with Opinio survey software. RESULTS: Sixty-four pharmacists completed the survey. Key findings of the study were that approximately 50% of pharmacists spend up to 25% of their time providing direct patient care and that not all adult oncology patients are receiving education by a pharmacist. CONCLUSIONS: Pharmacists provide patient education at the first treatment, change in therapy, and on request of another healthcare professional. Most cover administration, side effects, their prevention and management, and drug-interactions. Frequently used teaching methods include structured patient education delivery process, customized teaching for each patient, and repetition of key educational points.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".