Impact of a quality improvement program on primary healthcare in Canada: A mixed-method evaluation
Bibliographic record
Abstract
PURPOSE: Rigorous comprehensive evaluations of primary healthcare (PHC) quality improvement (QI) initiatives are lacking. This article describes the evaluation of the Quality Improvement and Innovation Partnership Learning Collaborative (QIIP-LC), an Ontario-wide PHC QI program targeting type 2 diabetes management, colorectal cancer (CRC) screening, access to care, and team functioning. METHODS: This article highlights the primary outcome results of an external retrospective, multi-measure, mixed-method evaluation of the QIIP-LC, including: (1) matched-control pre-post chart audit of diabetes management (A1c/foot exams) and rate of CRC screening; (2) post-only advanced access survey (third-next available appointment); and (3) post-only semi-structured interviews (team functioning). RESULTS: Chart audit data was collected from 34 consenting physicians per group (of which 88% provided access data). Between-group differences were not statistically significant (A1c [p=0.10]; foot exams [p=0.45]; CRC screening [p=0.77]; advanced access [p=0.22]). Qualitative interview (n=42) themes highlighted the success of the program in helping build interdisciplinary team functioning and capacity. CONCLUSION: The rigorous design and methodology of the QIIP-LC evaluation utilizing a control group is one of the most significant efforts thus far to demonstrate the impact of a QI program in PHC, with improvements over time in both QIIP and control groups offering a likely explanation for the lack of statistically significant primary outcomes. Team functioning was a key success, with team-based chronic care highlighted as pivotal for improved health outcomes. Policy makers should strive to endorse QI programs with proven success through rigorous evaluation to ensure evidence-based healthcare policy and funding.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".