Survey of community pharmacists regarding their role and desire for managing chemotherapy related toxicities in cancer patients.
Bibliographic record
Abstract
104 Background: There is little published about the readiness and needs of community pharmacists to manage chemotherapy related toxicities in cancer patients. A survey was conducted to understand community pharmacists’ current toxicity management practices and their education and communication needs in this area. Methods: A 21 question electronic survey was sent to community pharmacists in Ontario, Canada from April 1 – June 30, 2016. The survey asked about demographics, toxicity management behaviours/preferences, communication and training needs/preferences. Results: Out of 559 responses received, 167 were excluded due to ineligibility giving a final response of 392 surveys. The majority of respondents were full time pharmacists practicing for more than 10 years in community pharmacy. While many pharmacists reported providing assessment (80%), advice (92%) and/or monitoring (70%) at least sometimes, few reported providing assessment (10%), monitoring (10%) or advice (18%) routinely. Types of toxicities encountered and their frequency are summarized in Table 1. There was a high level of interest (96%) among the respondents in being involved in assessing and managing chemotherapy toxicity, however, only 13% reported that they felt sufficiently trained to do so. Conclusions: Community pharmacists encounter chemotherapy-related toxicities in their daily work. While there is a strong interest in managing toxicity symptoms, many community pharmacists feel that they are not adequately trained to do so. Continuing education programs for this provider group may improve toxicity management in community pharmacy settings. [Table: see text]
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".