Potential for patient-physician language discordance in Ontario
Bibliographic record
Abstract
BACKGROUND: Patient-Physician language discordance occurs when the patient and physician lack proficiency in the same language(s). Previous literature suggests language discordant clinical encounters compromise patient quality of care and health outcomes. The objective of this study was to quantify and visualize the linguistic and spatial mismatch between Ontario's population not proficient in English or French but proficient in one of the top five non-official languages and the physicians who are proficient in the same non-official language. METHODS: Using data from the 2006 Canadian census and the 2006 Canadian Medical Directory, we determined the number of non-English/non-French (NENF) speaking individuals and the number of Ontario physicians proficient in the top five non-official languages in each census division (CD) of Ontario. For each non-official language, we produced bi-variate choropleth maps of Ontario, broken down into the 49 CDs, to determine which CDs had the highest risk of language discordant clinical encounters. RESULTS: According to the 2006 Canadian census, the top five non-official languages spoken by Ontario's NENF population were: Chinese, Italian, Punjabi, Portuguese and Spanish. For each of the top five non-official languages, there were at least 5 census divisions with a NENF population speaking a non-official language without any primary care physicians proficient in that non-official language. The size of NENF populations within these CDs ranged from 10 individuals to 1,470 individuals. CONCLUSIONS: Understanding the linguistic capabilities of Ontario's immigrant population & the linguistic capabilities of Ontario's primary care physicians is essential to ensure equal access and quality of healthcare. As immigration continues to increase, we may find that the linguistic needs of Ontario's immigrant population diverge from the linguistic capabilities of Ontario's primary care physicians. Further research on the language discordance in Ontario is needed in order to reduce the risk of language discordant clinical encounters and the negative health outcomes associated with these encounters.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".