Nurse champions as leaders for the implementation of CoACT Collaborative Care
Bibliographic record
Abstract
This is the first paper describing the unit level champion role in order to implement the Collaborative Care framework as an evidence-based practice in the province of Alberta. The clear selection criteria of Unit Lead, funding (.2 FTE) that allows for the dedication of the role, support with various education, coaching from the project management team, and community of practices were suggested as important factors for successful utilization of Unit Leads to implement quality improvement initiatives in a large scale. Future initiatives may consider using a peer-leader champion as a change agent who is committed to the change initiative, credible and personally connected to the unit staff, possesses knowledge about the organizational culture, and develops a unit-tailored strategy via performance monitoring data to fully implement an evidence-based practice for quality care.
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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 | 0.053 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".