Ten tips for advancing a culture of improvement in primary care
Bibliographic record
Abstract
Embracing practice-based quality improvement (QI) represents one way for clinicians to improve the care they provide to patients while also improving their own professional satisfaction. But engaging in care redesign is challenging for clinicians. In this article, we describe our experience over the last 7 years transforming the care delivered in our large primary care practice. We reflect on our journey and offer 10 tips to healthcare leaders seeking to advance a culture of improvement. Our organisation has developed a cadre of QI leaders, tracks a range of performance measures and has demonstrated sustained improvements in important areas of patient care. Success has required deep engagement with both patients and clinicians, a long-term vision, and requisite patience.
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 | 0.093 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.015 | 0.041 |
| Insufficient payload (model declined to judge) | 0.007 | 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".