Oral Anticancer Medication Adherence, Toxicity Reporting, and Counseling: A Study Comparing Health Care Providers and Patients
Bibliographic record
Abstract
PURPOSE: Oral anticancer medications (OACMs) have created new treatment opportunities, but also challenges for patients and practitioners. We aimed to compare health care provider (HCP) and patient perceptions on OACM adherence, toxicity reporting, and patient educational needs. METHODS: An online survey for HCPs and paper survey for patients were analyzed using descriptive statistics. Bivariate analysis using the χ(2) test was used for some questions. RESULTS: There were 169 HCP and 143 patient responses; 91% of patients reported taking their OACMs as prescribed more than 75% of the time, but only 40% of HCPs believed their patients were as adherent; 97% of HCPs believed patients reported their adverse effects some or most of the time; 61% of patients reported toxicities sometimes, often, or very often, but 30% never or rarely reported; 66% of HCPs believed patients did not report toxicity because of fear of treatment interruption, compared with 2% of patients. HCPs (53%) and patients (62%) both believed adverse effect tolerance was a common reason not to report. Most HCPs (70%) believed patients reported adverse effects first to a nurse. Patients seemed to report equally to nurses (42%) and oncologists (38%). Both HCPs and patients favored paper-based educational materials and call-back programs. CONCLUSION: This study highlights disparities in patient and HCP perceptions of OACM adherence principles and toxicity reporting. Opportunities for improved patient education are identified, particularly around reporting significant toxicities. Different HCPs may benefit from complimentary counseling tools to encompass the entire spectrum of patient needs and provider practice.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".