Survey of radiation therapists’ current practices and perceptions of psychosocial and supportive care in Canada and Norway
Bibliographic record
Abstract
PURPOSE: Many cancer patients undergoing radiation therapy report unmet psychosocial needs, which can negatively impact their treatment outcomes and quality of life. This study explored the current practices and perceptions of radiation therapists (RTs) practicing in Canada and Norway with respect to addressing the psychosocial and supportive care (PSSC) needs of their patients. METHODS: A cross-sectional study was conducted using an online evidence-informed survey of RTs in Canada and Norway that focused on (1) demographics, (2) RTs' confidence level and perceptions related to PSSC, and (3) RTs' current practices related to PSSC. Descriptive statistics, chi-square tests, and Mann-Whitney U tests were used to summarize the sample and compare differences between countries. RESULTS: A total of 210 RTs completed the survey (Canada, n = 79; Norway, n = 131). RTs in both countries identified PSSC as an important aspect of quality care. Canadian RTs expressed a greater desire to expand their scope of practice in PSSC (p = 0.001). Norwegian RTs reported spending more time providing PSSC (mean: 8.3 h vs. 3.8 h; p < 0.001) and were satisfied with their current capacity. Barriers common to both countries included a lack of training and time constraints. Canadian RTs additionally reported limited institutional support. CONCLUSION: Significant differences exist in the current practices and perceptions of RTs in Canada and Norway regarding PSSC delivery. However, Canadian and Norwegian RTs agree that engaging in PSSC ensures the best care for patients undergoing radiation therapy. With enhanced training, knowledge translation of resources, and institutional support, RTs can better address the PSSC needs of their patients.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".