Partnership and Measurement: The Promise, Practice and Theory of a Successful Health Social Networking Strategy
Bibliographic record
Abstract
Patient health management (PHM) was launched as a promising paradigm to close care gaps, the inequities between usual and best care, for whole patient populations. PHM's core premise was that interventions of multidisciplinary, community-oriented partnerships that used repeated measurement and feedback of provider practices, clinical and economic outcomes and general communication of relevant health knowledge to all stakeholders would continuously make things better. This article reviews the evolution of PHM from its genesis in a series of casual hospital-based networks to its maturation in a province-wide, community-focused, clustered-lattice social network that facilitated the improved clinical and cost-efficient care and outcomes of whole patient populations. The factors underlying PHM's clinical and cost efficacy, specifically its patient-centric social networking structures and integral measurement and knowledge translation processes, offer continuing promise to optimally manage the care of our increasingly aged patient populations, with their high burden of chronic diseases and disproportionately large care gaps. In an era when patients are demanding and leading change, and governments are struggling fiscally, PHM's clinical efficacy and cost-efficiency are especially resonant. Things can be better.
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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 | 0.073 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.102 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".