From 'multicultural health' to 'knowledge translation'—rethinking strategies to promote language access within a risk management framework
Bibliographic record
Abstract
Introduction. There is compelling international evidence on the negative impacts of language barriers and reliance on untrained interpreters on health and healthcare. However, response to this evidence has been slow and uneven, and gains made over the years risk being eroded. This 'knowledge to action' gap is, however, not unique to the issue of language access. Methods. In one large Canadian health authority, a four stage knowledge translation (KT) strategy (getting the issue on the agenda; informing the response; guiding implementation; and changing provider practice) was developed to promote evidence-informed action to address language barriers. This multi-faceted strategy incorporated the principles of partnership with knowledge users, maintaining a focus on the evidence, phased introduction of evidence, synthesising evidence in context, and working within the conceptual framework of decision-makers. This approach reflected a shift from a 'multicultural health' to a 'risk management' approach in communicating with decision-makers, and integration of the issue of language access with already identified organisational priorities. Results. This collaborative strategy resulted in health system adoption of a unique evidence-informed model of trained health interpreter services, even though initiated during difficult economic times. Conclusion. Focused use of 'knowledge to action' strategies has the potential to promote evidence-informed action in provision of interpreter services.
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 | 0.127 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.015 | 0.062 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.005 | 0.033 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".