Patient Education Practices and Preferences of Radiation Oncologists and Interprofessional Radiation Therapy Care Teams: A Mixed-Methods Study Exploring Strategies for Effective Patient Education Delivery
Bibliographic record
Abstract
PURPOSE: Patients' understanding of radiation therapy (RT) and data regarding optimal approaches to patient education (PE) within radiation oncology (RO) are limited. We aimed to evaluate PE practices of radiation oncologists and interprofessional RT care team members to inform recommendations for delivering inclusive and accessible PE. METHODS AND MATERIALS: An anonymous survey was administered to all Radiation Oncology Education Collaborative Study Group members (10/5/22-11/23/22). Respondent demographics, individual practices/preferences, and institutional practices were collected. Qualitative items explored strategies, challenges, and desired resources for PE. Descriptive statistics summarized survey responses. The Fisher exact test compared PE practices by respondent role and PE timing. Thematic analysis was used for qualitative responses. RESULTS: One hundred thirteen Radiation Oncology Education Collaborative Study Group members completed the survey (28.2% response rate); RO attendings comprised 68.1% of respondents. Most practiced in an academic setting (85.8%) in North America (80.5%). Institution-specific materials were the most common PE resource used by radiation oncologists (67.6%). Almost half (40.2%) reported that their PE practices differed based on clinical encounter type, with paper handouts commonly used for in-person and multimedia for telehealth visits. Only 57.7% reported access to non-English PE materials. PE practices among radiation oncologists differed according to RT clinical workflow timing (consultation versus simulation versus first RT, respectively): one-on-one teaching: 88.5% versus 49.4% versus 56.3%, P < .01, and paper handouts: 69.0% versus 28.7% versus 16.1%, P < .01. Identified challenges for PE delivery included limited time, administrative barriers to the development or implementation of new materials or practices, and a lack of customized resources for tailored PE. Effective strategies for PE included utilization of visual diagrams, multimedia, and innovative education techniques to personalize PE delivery/resources for a diverse patient population, as well as fostering interprofessional collaboration to reinforce educational content. CONCLUSIONS: Radiation oncologists and interprofessional RO team members engage in PE, with most using institution-specific materials often available only in English. PE practices differ according to clinical encounter type and RT workflow timing. Increased adoption of multimedia materials and partnerships with patients to tailor PE resources are needed to foster high-quality, patient-centered PE delivery.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".