An evaluation of the quality of care of diabetic patients with multimorbidity at nurse practitioner-Led clinics in the North East and North Simcoe Muskoka LHINs
Bibliographic record
Abstract
A multiple case study was undertaken with five Nurse Practitioner-Led Clinics (NPLCs) in in the North East and North Muskoka Simcoe Local Health Integration Networks in Ontario, Canada. The purpose was to evaluate the quality of care in NPLCs, specifically to address the following research questions: What is the relationship between organizational processes in the NPLC model and the care of diabetic patients with multimorbidity? To what extent are clinical practice guideline indicators for diabetes care being met for patients with multimorbidity in five NPLCs? How do nurse practitioners practicing at NPLCs evaluate the impact of the NPLC model on the quality of the care they provide to diabetic patients with multimorbidity? The research was guided by the Donabedian Quality Framework. The study included three types of data collection. First, documents related to NPLCs were collected and the impact of the nurse practitioner (NP) as primary care provider was analyzed. Then the Chronic Care Model was used to guide the development of a chart audit in each of the five NPLCs, conducted on 30 charts per clinic (n= 150) for non-pregnant, adult diabetic patients with multimorbidity. Analysis determined relationships between diabetes indicator scores and organizational and patient data. Next, 8 NPs from 4 of the 5 NPLCs participated in interviews, guided by semi-structured questions arising from the document review and chart audit analysis. Interpretive description methods were used to analyze the transcripts and identify themes. A cross-case analysis was conducted to compare the results of all analyses across NPLCs. Themes emerging from this multiple case analysis confirmed that the NP is the primary care provider and clinical leader at NPLCs, which has contributed positively to the quality of care in NPLCs. NPLCs have suffered from poor NP recruitment and retention. This, along with insufficient health care resources in the communities where NPLCs are located and high patient vulnerability, has had a negative influence on the quality of care. Strategies including mentoring and changes to funding offer possible solutions to improve the quality of care at NPLCs. (336 words)
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".