Strategies for improved French-language health services: Perspectives of family physicians in northeastern Ontario.
Bibliographic record
Abstract
OBJECTIVE: To identify strategies to improve the quality of health services for Francophone patients. DESIGN: A series of semistructured key informant interviews. SETTING: Northeastern Ontario. Participants A total of 18 physicians were interviewed. Ten physicians were interviewed in French, 7 physicians were women, and 10 physicians were located in urban communities. METHODS: Purposive and snowball sampling strategies were used to conduct a series of semistructured key informant interviews with family physicians practising in communities with a large Francophone population. Principles of grounded theory were applied, guided by a framework for patient-professional communication. Results were inductively derived following an iterative data collection–data analysis process and were analyzed using a detailed thematic approach. MAIN FINDINGS: Respondents identified several strategies for providing high-quality French-language health services. Some were unique to non–French-speaking physicians (eg, using appropriate interpreter services), some were unique to French-speaking physicians (eg, using a flexible dialect), and some strategies were common to all physicians serving French populations (eg, hiring bilingual staff or having pamphlets and posters in both French and English). CONCLUSION: Physicians interviewed for this study provided high-quality health care by attributing substantial importance to effective communication. While linguistic patient-to-physician concordance is ideal, it might not always be possible. Thus, conscious efforts to attenuate communication barriers are necessary, and several effective strategies exist.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".