Patient and Provider Satisfaction With a Geomapping Tool for Finding Community Family Physicians in Ontario, Canada: Cross-Sectional Online Survey Study
Bibliographic record
Abstract
BACKGROUND: Language-concordant health care, or health care in a patient's language of choice, is an important element of health accessibility that improves patient safety and comfort and facilitates an increased quality of care. However, prior research has found that linguistic minorities often face higher travel burdens to access language-concordant care compared to the general population. OBJECTIVE: This study intended to assess patient experiences and satisfaction with an online interactive physician map that allows patients to find family physicians who speak their preferred language in and around Ottawa, Ontario, Canada, as a means of identifying areas of improvement. METHODS: This study used an online survey with questions related to user satisfaction. Responses to Likert-scale questions were compiled as summary statistics and short-answer responses underwent thematic analysis. The study setting was Ottawa and Renfrew County, Ontario, and the surrounding region, including the province of Quebec. RESULTS: A total of 93 respondents completed the survey and self-identified as living in Ontario or Quebec. Overall, 57 (61%) respondents were "very satisfied" or "somewhat satisfied" with the map, 16 (17%) were "neither satisfied nor dissatisfied," and 20 (22%) were "very dissatisfied" or "somewhat dissatisfied." We found no significant differences in satisfaction by preferred language, age group, physician attachment, or intended beneficiary. A total of 56 respondents provided short-answer responses to an open-ended question about map improvements. The most common specific suggestion was to show which physicians are accepting new patients (n=20). Other suggestions included data refreshes (n=6), user interface adjustments (n=23), and additional languages (n=2). Some participants also provided positive feedback (n=5) or expressed concern with their inability to find a family physician (n=5). Several comments included multiple suggestions. CONCLUSIONS: While most patients were satisfied with the online map, a significant minority expressed dissatisfaction that the map did not show which family physicians were accepting new patients. This suggests that there may be public interest in an accessible database of which family physicians in Ontario are currently accepting new patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".