Bibliographic record
Abstract
Understanding the issues in advancing quality in Canadian primary healthcare requires some comprehension of systems theory as it applies to healthcare, as well as an understanding of the context of Canadian primary healthcare, particularly the roles of family physicians. With that background, one is then prepared to appreciate the current challenge in advancing the quality agenda, where provider learning of the content and skills of quality improvement and leading change, models of community or regional governance, and infrastructure such as information technology and its necessary supports for interoperability with other healthcare systems, are all primitive. For primary care providers, driven in large part by their desire to improve the health of the individuals and populations they serve, "Framework for Advancing Improvement in Primary Care" is a welcome guide for direction in how to begin their quality journey. The framework provides the map with the destination (the Institute for Healthcare Improvement's Triple Aim) and roads to get there (six characteristics of high-performing primary healthcare systems). Finally, our ability to improve the system builds from partnerships with our practice citizens - we need to move beyond the patient care construct.
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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.019 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.036 | 0.041 |
| Insufficient payload (model declined to judge) | 0.007 | 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".