Barriers to accessing radiation therapy in Canada: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Radiation therapy (RT) is effective treatment for curing and palliating cancer, yet concern exists that not all Canadians for whom RT is indicated receive it. Many factors may contribute to suboptimal use of RT. A review of recent Canadian literature was undertaken to identify such barriers. METHODS: MEDLINE, CINAHL, and EMBase databases were used to search keywords relating to barriers to accessing or utilizing RT in Canada. Collected abstracts were reviewed independently. Barriers identified in relevant articles were categorized as relating to the health systems, patient socio-demographic, patient factors, or provider factors contexts and thematic analysis performed for each context. RESULTS: 535 unique abstracts were collected. 75 met inclusion criteria. 46 (61.3%) addressed multiple themes. The most cited barriers to accessing RT when indicated were patient age (n = 26, 34.7%), distance to treatment centre (n = 23, 30.7%), wait times (n = 22, 29.3%), and lack of physician understanding about the use of RT (n = 16, 21.6%). CONCLUSIONS: Barriers to RT are reported in many areas. The role of provider factors and the lack of attention to patient fears and mistrust as potential barriers were unexpected findings demanding further attention. Solutions should be sought to overcome identified barriers facilitating more effective cancer care for Canadians.
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 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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.011 | 0.027 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".