Quality of Care for Patients With Diabetes and Mulitmorbidity Registered at Nurse Practitioner-Led Clinics
Bibliographic record
Abstract
Background Nurse Practitioner-Led Clinics are a new model of primary healthcare in Ontario. Nurse Practitioner-Led Clinics are distinctive in that nurse practitioners are the primary care providers working with an interprofessional team. There have been no evaluations of the quality of care within the Nurse Practitioner-Led Clinic model. Purpose Evaluation of the Nurse Practitioner-Led Clinic model, specifically for complex clinical presentations, will provide insights that may be used to inform improvements to the delivery of care in the Nurse Practitioner-Led Clinics. The aim of this study was to evaluate the extent to which diabetes care was complete and to determine the impact of organizational tools, including electronic medical record tracking, diabetes care template, and referral to community programs, on the completeness of care for patients with diabetes and multimorbidity at Nurse Practitioner-Led Clinics. Methods An audit of 30 charts was conducted at five different Nurse Practitioner-Led Clinics (n = 150) for patients with diabetes and at least one other chronic condition. Indicators included patient and organizational characteristics as well as diabetes care items taken from diabetes clinical guidelines. Results Overall, care for patients with diabetes and multimorbidity in Nurse Practitioner-Led Clinics was complete. However, there were no significant associations between patient or organizational characteristics and the extent to which diabetes care was complete.
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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".