Bibliographic record
Abstract
Successful next-generation healthcare must deliver timely access and quality for an aging population, while simultaneously promoting disease prevention and managing costs. The key factors for sustained success are a culture with aligned goals and values; coordinated team care that especially engages with physicians and patients; practical information that is collected and communicated reliably; and education in the theory and methods of collaboration, measurement and leadership. Currently, optimal population health is challenged by a high prevalence of chronic disease, with large gaps between best and usual care, a scarcity of health human resources - particularly with the skills, attitudes and training for coordinated team care - and the absence of flexible, reliable clinical measurement systems. However, to make things better, institutional models and supporting technologies are available. In the short term, a first step is to enhance the awareness of the practical opportunities to improve, including the expansion of proven community-based disease management programs that communicate knowledge, competencies and clinical measurements among professional and patient partners, leading to reduced care gaps and improved clinical and economic outcomes. Longer-term success requires two additional steps. One is formal inter-professional training to provide, on an ongoing basis, the polyvalent human resource skills and foster the culture of working with others to improve the care of whole populations. The other is the adoption of reliable information systems, including electronic health records, to allow useful and timely measurement and effective communication of clinical information in real-world settings. A better health future can commence immediately, within existing resources, and be sustained with feasible innovations in provider and patient education and information systems. The future is now.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".