Bibliographic record
Abstract
Continuity and timely access are hallmarks of high-quality primary care and are important considerations for urgent concerns that present both during the day and after-hours. It can be especially difficult to ensure continuity of primary care after-hours in urban settings where walk-in clinics offer patients easy and convenient access. Patients of our large, multisite primary care practice in inner-city Toronto, Canada were reporting that they were not easily able to access after-hours care from their team without having to use outside services. In partnership with patients, we combined the Model for Improvement with Experience-Based Design methodology to address the issue of poor access to after-hours care. We did a root cause analysis to isolate the causes of the local problem, using a variety of capture tools designed to incorporate the patient voice. Then, patients and providers codesigned two Plan-Do-Study-Act (PDSA) cycles aimed to increase the ease of accessing after-hours care. Key actions included a redesign of our after-hours advertisement and communication of the material in multiple formats. Following these PDSA cycles, the team saw a 26%, 23% and 17% increase in awareness of weekday evening clinics, weekend clinics and after-hours phone services, respectively, and a 16% increase in the proportion of patients reporting that it was very or somewhat easy to get care during the evening, on the weekend or on a holiday from their care team. Measures continued to improve and improvements have been sustained 3 years later. Our success highlights the effectiveness of partnering with patients to improve access to primary care.
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 | 0.029 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".