Docmapper evaluation: User experience with a tool for finding language-concordant care from community family physicians.
Bibliographic record
Abstract
Context: Language-discordant healthcare can lead to worse health outcomes, including an increased risk of mortality in some contexts. In an earlier study, we determined that French-only speakers in some parts of Ottawa face higher travel burdens to access language-concordant care compared to English-speakers. To help address this discrepancy, we developed a web-based mapping tool intended to help patients find language-concordant primary care from community-based family physicians in Ottawa, Ontario. Objectives: To assess patient experiences and user satisfaction with an online interactive physician map (www.docmapper.ca; www.trouvezunmedecin.ca) as a means of identifying areas of improvement. Study Design and Analysis: A cross-sectional web-based survey. Responses to Likert scale questions were reported as summary statistics, and short-answer responses underwent thematic analysis. Setting or Dataset: Ottawa and Renfrew County, Ontario, and the surrounding region including Quebec. Population Studied: A total of 93 respondents (including patients and providers) who completed an online survey and self-identified as living in Ontario or Quebec. Intervention/Instrument: An online survey with questions related to user satisfaction. Outcomes Measured: Patient/User experience. Results: A total of 93 respondents completed an online survey and self-identified as living in Ontario or Quebec. Overall, 57 of the respondents (61.3%) were “Very Satisfied” or “Somewhat Satisfied” with the map, 16 (17.2%) were “Neither satisfied nor dissatisfied,” and 20 (21.5%) were “Very Dissatisfied” or “Somewhat Dissatisfied.” We found no significant differences in satisfaction by preferred language, age group, physician attachment, or intended beneficiary. In addition, 56 respondents provided short-answer responses to an open-ended question about improvements to the map. The most common specific suggestion for improvement was to show which physicians are accepting new patients (n=20). Conclusion: While most users were satisfied with the online map, a significant minority expressed dissatisfaction that the map did not show which family physicians were currently accepting new patients. This suggests that there may be public interest in an accessible database of family physicians in Ontario who are 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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".