The Impact of Linguistic Concordance and the Active Offer of French Language Services on Patient Satisfaction
Bibliographic record
Abstract
Communication is essential to providing quality primary care. Linguistic concordance between patients and physicians has been linked to improved health outcomes and greater patient satisfaction. Although Canadian Francophones often struggle to access linguistics concordant health services, the concept of the active offer of French Language Services (FLS) has emerged as a means of ensuring the availability of such services and improving the francophone patient experience. However, the impact of language concordance and the active offer of FLS on patient satisfaction among Ontario Francophones remain largely unknown. Patient satisfaction surveys were collected as part of a continuing education program targeted at family physicians in Northeastern Ontario. Participating physicians distributed patient surveys consisting of select patient satisfaction questions from the Physicians Achievement Review (PAR) and select questions from the Active Offer of French Language Services in Minority Context Measure. Valid surveys were received from 235 patients. Just under half of these (44%) identified as Francophones, 62.6% had a French-speaking family physician; however, only 17.2% reported regularly speaking in French with their family physician. As hypothesized, there was a consistent tendency for Francophones who experience stronger linguistic concordance with their family physician to report higher satisfaction scores. Francophones who regularly speak French with their family physicians were more satisfied ( = 4.63) than those who rarely/never speak French ( = 4.29, F(1; 83) = 4.852; p < 0.05). There was also a statistically significant interaction between the patients' language of preference and the service language. Francophones who prefer French and regularly speak it with their family physician (linguistic concordance; adj= 4.82) were significantly more satisfied than those who prefer French yet rarely/never speak it (linguistic discordance; adj= 4.06, F(1; 75) = 11.950; p < 0.001). Furthermore, a positive correlation between patient satisfaction and the active offer was observed in Francophones (r = 0.49, p<0.001). The present findings provide evidence of the impact of linguistically adapted health care services on the satisfaction of Ontario Francophones and suggest that patient satisfaction may be improved through the active offer of FLS. A larger and more diverse sample is required to confirm these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".