Managing chemotherapy-related toxicities in the community setting: A survey of pharmacists in Ontario
Bibliographic record
Abstract
BACKGROUND: Toxicity management is a challenge with cancer treatment, including oral anticancer drugs. A review of claims data showed that a majority of publically funded oral anticancer drugs were filled in the community where pharmacists may not necessarily possess the specialized knowledge, skills, and experience required to provide effective patient care. A survey of community pharmacists in Ontario was conducted to identify the behaviours and preferences of community pharmacists specific to the management of treatment-related toxicities in order to standardize cancer care in this area. METHODS: An electronic questionnaire was distributed to approximately 5000 community pharmacists. The 21-question survey gathered information on the demographic profile of the pharmacists, basic geographic and socioeconomic variables associated with their practice setting, current toxicity management practices, education and training needs, and preferences for communicating with other providers. RESULTS: Of 349 pharmacists, almost all (94.9%) were interested in managing chemotherapy-related toxicities as part of their work, but the majority (77.1%) did not feel that their current level of pharmacy training has provided them with an oncology education sufficient for the demands of their practice. Approximately 52% of respondents indicated that they have reached out to the health care provider at a cancer centre, and of those, 72.7% reported that their questions were resolved within 48 h. More than half of all survey respondents (53.9%) indicated that they would prefer to receive a response within 12 h from cancer centres. CONCLUSIONS: The results of this study support the need to provide community pharmacists with oncology-specific training and timely correspondences from providers at prescribing institutions in order to manage toxicities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".