Using Quality Indicators to Evaluate the Effect of Implementing an Enhanced Collaborative Care Model among a Community, Primary Healthcare Practice Population
Bibliographic record
Abstract
Over the past decade, Nova Scotia has been making important changes to strengthen its primary healthcare (PHC) system. Here we present the results from an observational, retrospective study evaluating the effect of an enhanced collaborative care model, which included team building and the addition of a nurse practitioner (NP) to the team, on the quality of healthcare delivery among a community, PHC population. To guide the evaluation, we used a broad range of national and provincially identified clinical quality-of-care indicators targeting a wide range of preventive and chronic disease management care (targeting process of care, and immediate and secondary outcomes). A total of 392 patient charts were audited (197 pre-period; 195 post-period). Patients included the full spectrum of the practice population - the young, the old, those with chronic conditions and others without. Results support the increasing body of evidence, which indicates improved chronic disease management among patients with targeted chronic conditions, particularly patients diagnosed with diabetes, who receive care through a collaborative practice where a NP is part of the team. In addition, these results also demonstrate the beginning of better preventive care among all patients.
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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 | 0.024 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".