From theory to practice: an illustrative case for selecting evidence-based practices and building implementation capacity in three Canadian health jurisdictions
Bibliographic record
Abstract
Using principles of Applied Implementation Science, this paper examines strategies for systematically selecting and operationalising National clinical practice guidelines and intentionally creating implementation supports to ensure high fidelity use and sustainable application and outcomes. In the spirit of participatory action research, key pan-Canadian stakeholders including funders, researchers, providers and patients were brought together around shared interests of optimal patient outcomes, improved provider performance, and continuous quality improvement of practices targeting the three most common secondary complications that patients with spinal cord injuries experience: pressure ulcers, bladder dysfunction and pain. Implementation capacity development at site and project levels will be described.
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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 | 0.060 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.071 | 0.032 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.011 | 0.009 |
| 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".