Assessment of language needs and resource use among Canadian immigrant patients with cancer.
Bibliographic record
Abstract
77 Background: Canada's foreign-born population is estimated to reach up to 30% by 2036. Immigrants diagnosed with cancer can face poorer cancer outcomes compared to non-immigrants due to language barriers affecting physician-patient communication and non-equivalent interpretation by untrained translators. Reducing this disparity is a growing challenge due to continued high immigration rates. As Canadian data is lacking, we explored the language needs and resource utilization of Canadian cancer patients. Methods: Adult cancer survivors from Princess Margaret Cancer Centre were surveyed in English on their socio-demographics, perceived communication difficulties, and interpreter use. Results: Of 470 patients, 45% were foreign-born; 37% self-identified as immigrants; 78% spoke mostly English at home. Among self-identified immigrants, median age at diagnosis was 60 years, 46% were female, and 59% completed post-secondary education. Top three countries of origin were China (23%), India (10%), and Italy (9%). Although 73% of immigrants reported English as their most comfortable language in which to receive healthcare information, 18% reported difficulties communicating with the doctor in English at a few visits or more, 14% indicated some discomfort communicating with their oncologist in English, and 4% of immigrants felt their cancer care was affected by a language barrier. Twenty-six percent of immigrants required interpretation, with 91% employing family, 7% utilizing professional interpreters, and only 19% have ever used the institutional language services. Conclusions: Although most immigrants identify English as their most comfortable language for healthcare delivery, some still experience communication difficulties with their oncologist during at least a few visits. However, most patients felt comfortable communicating with their oncologists and do not feel their cancer care is affected. Most relied on family members to interpret, while local hospital language resources were largely underutilized. Strategies for promoting professional translation service use may help improve immigrants’ experience, caregivers’ burden, and enhance physicians’ ability to counsel patients directly.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| 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".