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 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 | 0.099 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".