MétaCan
Menu
Back to cohort
Record W4392346620 · doi:10.1016/j.ijrobp.2024.02.023

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

2024· article· en· W4392346620 on OpenAlexaff
Jie Jane Chen, Anna M. Brown, Allison E. Garda, Ellen Kim, Sarah McAvoy, Subha Perni, Michael K. Rooney, Kevin Shiue, Kristi L. Tonning, Laura Warren, Daniel W. Golden, Jennifer Croke

Bibliographic record

VenueInternational Journal of Radiation Oncology*Biology*Physics · 2024
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRespondentMedicineThematic analysisRadiation TherapistCurriculumFamily medicineDescriptive statisticsDemographicsMedical educationRadiation therapyPsychologyQualitative researchInternal medicine

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.029
GPT teacher head0.455
Teacher spread0.426 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2024
Admission routes1
Has abstractno

Explore more

Same venueInternational Journal of Radiation Oncology*Biology*PhysicsSame topicAdvances in Oncology and RadiotherapyFrench-language works237,207