Patient-Provider Partnerships in Healthcare: Enhancing Knowledge Translation and Improving Outcomes
Bibliographic record
Abstract
In the complex health arena, a key proposition is that no person acting alone is as effective as a team to drive best practices and outcomes. Another key factor supporting best outcomes is access to the best information to support best choices. Currently, stakeholders suffer from a paucity of real-world knowledge of actual practices and outcomes that allows care gaps to go undiscovered. A body of evidence indicates that measurement and timely feedback of actual practices can decrease the gaps between usual and best care. This is driven by the stakeholders' desire to be the best they can be, and it is enabled by the measured knowledge of where practices fall short of gold standards. The addition of patient partners to such communities of care offers promise of further acceleration and broader impact of knowledge translation and associated beneficial outcomes. For example, in the Improving Cardiac Outcomes in Nova Scotia (ICONS) community-based heart disease project, there was a marked decrease in rates of re-hospitalization over the five-year course of the project. This improvement was only very weakly, or not at all, related to traditional risk factors, such as the presence of multiple illnesses or older age, or to the use of efficacious medical therapies. However, ICONS provided an extensive and repeated multimedia communication among patients, families and providers of project goals, strategy and general news, as well as repeated measurements of practices and outcomes. One outcome of this shared knowledge may have been the reduced need for re-hospitalization. While exact cause-and-effect relationship remain uncertain, patient-provider integrated health networks appear feasible and offer promise for efficient knowledge creation and its population-effective translation. The model and its implementation may be improved by testing further locally responsive initiatives in innovative partnership clusters and by training more personnel resources in inter-professional settings.
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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 | 0.147 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.004 | 0.051 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.031 | 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".