Quality improvement challenges encountered in primary care for diabetes management in the province of Quebec, Canada
Bibliographic record
Abstract
Context: Nearly 80% of the healthcare for patients with type 2 diabetes mellitus (T2DM) is provided by primary healthcare (PHC) teams. However, several barriers persist in providing diabetes care according to the recommendations from practice guidelines. Since 2016, a large-scale quality improvement collaborative, COMPAS+, has been implemented across the province of Quebec (Canada) to support improving chronic disease management in PHC. This program helped identify challenges and solutions to improve T2DM care in collaboration with practitioners, patients, and managers. Objective: To describe what PHC teams found as the most important gaps in T2DM care and the main causes of these quality problems. Study design and analysis: Seven T2DM COMPAS+ QI workshops were delivered before the COVID-19 pandemic in 2 regions of the Quebec province. Detailed workshop reports were qualitatively analyzed using content analysis. Setting: Workshops were offered to PHC teams from local regional networks responsible together for providing services to a specific population. Population: All PHC professionals from the local regional network were invited to participate and targeted managers and patient partners were also invited. Intervention/instrument: Group reflection on administrative regional data and root cause analysis process were used to identify the most important gaps and their underlying causes. Results: Gaps in T2DM care were grouped into three themes: 1) lack of coordination and integration of services; 2) lack of preventive services for diabetes and pre-diabetes; and 3) lack of integration of the patient-partner approach to support diabetes self-management. Each gap was influenced by multiple underlying causes such as implementation climate, lack of understanding of all team members’ professional roles, lack of leadership and process planning, lack of knowledge on selfmanagement support and person-centered case management, and lack of structure, processes, and tools to coordinate teamwork effectively. Proposed solutions were providing education and training to PHC professionals, using champions, and implementing clinical care pathways and available tools to improve person-centered case management. Conclusion: Recommendations can be formulated to implement these successful change strategies at the provincial level to improve T2DM management in PHC.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".