Bibliographic record
Abstract
Partnerships focused on best practice implementation have led to major improvements in the quality of care for people with complex conditions. Two cases will be highlighted in this chapter: care of people with leg ulcers and those with congestive heart failure (CHF). The synthesis of both external and local evidence played a key role in the development and implementation of the evidence-informed approaches and provided the critical context to support reorganization of the existing care and service delivery models that produced demonstrable benefits for patients and health services. The cases illustrate that, with a collaborative partnership approach along with a systematic and transparent enquiry process, best practice evidence can be aligned with the local context and implemented. Being transparent allows replication with other practice issues and greatly supports practice and policy changes.
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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 | 0.039 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.019 | 0.027 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".