Framework for Advancing Improvement in Primary Care
Bibliographic record
Abstract
A consistent feature of effective healthcare delivery systems is a strong and well-integrated primary care sector. This paper presents a framework that describes the key elements of high-performing primary care and the supports required to attain it. The framework was developed by the Quality Improvement and Innovation Partnership in Ontario (now part of Health Quality Ontario) to guide the process of primary care transformation. The first section of this paper presents and describes the framework, the second proposes implementation strategies and the third identifies system-level structures and policies needed to support primary care transformation. The framework has three components: (1) the major constituencies that primary care serves – patients, families and their local communities; (2) the desired outcomes of primary care (better health, better care, better value); and (3) the attributes that will enable primary care organizations to attain these outcomes. These attributes are a population focus, patient engagement, partnerships with health and community services, innovation, performance measurement and quality improvement and team-based care.Proposed transformation strategies include building system capacity and capability, ensuring access to resources, providing support from coaches and employing effective spread and sustainability strategies. Broader system-level structures and policies necessary to support and sustain a high-performing and continually improving primary care sector include clear goals; a comprehensive approach to performance measurement; systematic evaluation of innovation; funding incentives aligned with quality outcomes; a system of local primary care organizations; support for inter-professional teams; funding for research to inform primary care policy, management and practice; patient enrolment with primary care providers; and mechanisms to support coordination and integration.
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 | 0.073 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".