Language barriers: use of regular medical doctors by Canada's official language minorities.
Bibliographic record
Abstract
OBJECTIVE: To assess use of regular medical doctors (RMDs), as well as awareness and use of telephone health lines or telehealth services, by official language minorities (OLMs) in Canada. DESIGN: Analysis of data from the 2006 postcensal survey on the vitality of OLMs. SETTING: Canada. PARTICIPANTS: In total, 7691 English speakers in Quebec and 12 376 French speakers outside Quebec, grouped into those who experienced language barriers and those with no language barriers. MAIN OUTCOME MEASURES: Health services utilization (HSU) by the presence of language barriers; HSU measures included having an RMD, use of an RMD's services, and awareness of and use of telephone health lines or telehealth services. Multivariable models examined the associations between HSU and language barriers. RESULTS: After adjusting for age and sex, English speakers residing in Quebec with limited proficiency in French were less likely to have RMDs (adjusted odds ratio [AOR] 0.66, 95% CI 0.50 to 0.87) and to use the services of their RMDs (AOR 0.65, 95% CI 0.50 to 0.86), but were more likely to be aware of the existence of (AOR 1.50, 95% CI 1.16 to 1.93) and to use (AOR 1.43, 95% CI 0.97 to 2.11) telephone health lines or telehealth services. This pattern of having and using RMDs and telehealth services was not observed for French speakers residing outside of Quebec. CONCLUSION: Overall we found variation in HSU among the language barrier populations, with lower use observed in Quebec. Age older than 45 years, male sex, being married or in common-law relationships, and higher income were associated with having RMDs for OLMs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".