Active offer of health services in French in Ontario: Analysis of reorganization and management strategies of health care organizations
Bibliographic record
Abstract
BACKGROUND: The availability of health services in French is not only weak but also inexistent in some regions in Canada. As a result, estimated 78% of more than a million of Francophones living in a minority situation in Canada experience difficulties accessing health care in French. To promote the delivery of health services in French, publicly funded organizations are encouraged to take measures to ensure that French-language services are clearly visible, available, easily accessible, and equivalent to the quality of services offered in English. OBJECTIVE: This study examines the reorganization and management strategies taken by health care organizations in Ontario that provide health services in French. METHODS: Review and analysis of designation plans of a sample of health care organizations. RESULTS: Few health care organizations providing services in French have concrete strategies to guarantee availability, visibility, and accessibility of French-language services. CONCLUSIONS: Implementation of the active offer of French-language services is likely to be difficult and slow. The Ontario government must strengthen collaboration with health care organizations, Francophone communities, and other key actors participating in the designation process to help health care organizations build capacities for the effective offer of French-language services.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".