Results of a Mixed-Methods Evaluation of Partnerships for Health: A Quality Improvement Initiative for Diabetes Care
Bibliographic record
Abstract
PURPOSE: Quality improvement (QI) initiatives have been implemented to facilitate transition to a chronic disease management approach in primary health care. However, the effect of QI initiatives on diabetes clinical processes and outcomes remains unclear. This article reports the effect of Partnerships for Health, a QI program implemented in Southwestern Ontario, Canada, on diabetes clinical process and outcome measures and describes program participants' views of elements that influenced their ability to reach desired improvements. METHODS: Part of an external, concurrent, comprehensive, mixed-methods evaluation of Partnerships for Health, a before/after audit of 30 charts of patient of program physicians (n = 35) and semistructured interviews with program participants (physicians and allied health providers) were conducted. RESULTS: The proportion of patients (n = 998) with a documented test/examination for the following clinical processes significantly improved (P ≤ .005): glycosylated hemoglobin (A1c), cholesterol, albumin-to-creatinine ratio, serum creatinine, glomerular filtration rate, electrocardiogram, foot/eye/neuropathy examination, body mass index, waist circumference, and depression screening. Data showed intensification of treatment and significant improvement in the number of patients at target for low-density lipoprotein (LDL) and blood pressure (BP) (P ≤ .001). Mean LDL and BP values decreased significantly (P ≤ .01), and an analysis of patients above glycemic targets (A1c >7% at baseline) showed a significant decrease in mean A1c values (P ≤ .01). Interview participants (n = 55) described using a team approach, improved collaborative and proactive care through better tracking of patient data, and increased patient involvement as elements that positively influenced clinical processes and outcomes. CONCLUSIONS: QI initiatives like Partnerships for Health can result in improved diabetes clinical process and outcome measures in primary health care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".