Bridging the information gap: A scoping review of radiation oncology patient education scholarship.
Bibliographic record
Abstract
e23164 Background: Patient education (PE), the use of educational interventions to improve patient knowledge of healthcare, has been shown to reduce patient anxiety and facilitate shared decision making. In contrast to many other medical specialties, the state of radiation oncology (RO) PE is unclear. This scoping review aims to characterize the state of ROPE peer-review scholarship to describe strengths, identify gaps and guide future ROPE research. Methods: Predetermined medical subject headings and search terms were used to search PubMed for ROPE articles. Additional terms were identified from these articles and the search was repeated until no further articles were found. In-press articles in major RO and medical education (ME) journals were searched. English language articles published after 1/1/1990 that had a primary ROPE focus were included. Two independent reviewers performed the search and determined eligibility. Articles were categorized using the most predominant step of Kern’s six-step conceptual framework for ME curriculum development and the country and year of publication. Results: Of 11,756 identified articles, 182 met inclusion criteria. Articles per year increased over time (p < 0.01): 1990-1994 (5.5%); 1995-1999 (11.0%); 2000-2004 (13.7%); 2005-2009 (10.4%); 2010-2014 (25.8%); 2015-2019 (33.5%). Conceptual framework categorization was as follows: problem identification (22.0%); needs assessment (23.6%); goals and objectives (4.9%); education strategies (9.9%); implementation (7.7%); evaluation and feedback (31.9%). Regional breakdown was as follows with some articles spanning regions: Africa (1.1%); South America (2.2%); Asia (6.0%); Australia (15.4%); Europe (30.2%); North America (45.6%). Conclusions: Nearly half of articles are primarily problem identification or needs assessment (45.6%) which are descriptive in nature and only identify or clarify ROPE needs. Additionally while these results are limited to English language scholarship, there are significant regional maldistributions. Although there is an encouraging trend of increasing ROPE publications over time, this study identifies opportunities to further develop, implement and evaluate ROPE scholarship.
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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 | 0.063 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.051 | 0.044 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".