Improving Access to Health Services in French: The Power of Networking and Knowledge Mobilization, a Proven Canadian Model
Bibliographic record
Abstract
Language barriers have a detrimental impact on access to health services and compromise patients’ safety. The purpose of this article is to describe and evaluate how the Société Santé en français (SSF) and the 16 French Language Health (FLH) networks used networking and knowledge mobilization for improving access to health services in French for Francophone and Acadian minority communities (FAMC). Method: Data was extracted from the 2013-2018 program’s reports and evaluation. Results confirmed that networking effectively mobilized key partners for better access to health services in French regardless of location. This access increased incrementally as a function of the customized level of support provided to the system by the SSF and the FLH networks and, to a lesser extent, when FAMC represented over 3.5% of the total population in the region, province or territory. Networking and knowledge mobilization contribute to augmenting access to safe, quality health services in French for FAMC.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".