Patient-reported outcomes in a linguistically diverse cancer population: Addressing barriers to access.
Bibliographic record
Abstract
168 Background: The census metropolitan area served by BC Cancer is Vancouver which has a population of 2.4 million. Census Canada noted the mother tongue learned in childhood and still spoken was: 54% English, 17% Indo-European, 16% Chinese, 10% Indo-Iranian, 3% English+other language, 2% other. At BC Cancer, implementation of patient reported outcomes (PROs) was launched 1 year ago with consideration of preferred language. Our goal was to examine the implementation of PROs with respect to equity of access and language preferences. Methods: BC Cancer Vancouver implemented PROs including the Edmonton Symptom Assessment Scale Revised, Canadian Problem Checklist and EQ5D in April 2021 using a web-based platform. A 1-year cohort was reviewed for patients’ mother tongue and the language used for the PROs. Qualitative interviews were undertaken for non-English mother tongue patients to ask for preferred language of PROs and reasons for the choice if mother tongue was not selected. Results: 86 patients were enrolled in the PRO program. Baseline characteristics: 66% female, age 30% < 60/ 31% 60-69/ 39% >70, primary site 54% lung/ 34% colorectal/ 18% other. Ethnic origin: 53% North American and European, 40% East and Southeast Asian, 2% Latin/Central and South American, 3% South Asian, 1% Indigenous, 1% other. Mother tongue: 52% English, 48% non-English (39% English proficient, 9% low English proficiency). Patient preferred language for PROs: 81% English, 11% Chinese, 7% other. Reasons for non-English mother tongue preference for English language included comfort with written English, use of family assistance completing questionnaires for both language and familiarity with electronic devices. Conclusions: In the Vancouver linguistically diverse cancer population, the majority of patients preferred English language PROs. Reasons for this include proficiency in the written language and the use of family members to complete the questionnaires. With 39% of patients over the age of 70 in our study, the use of electronic devices for completion may have introduced an additional barrier to access. In clinic iPad questionnaires with nursing and care aide support have been introduced to help address this gap. Clinical trial information: NCT05057234.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".