Foundational Change Strategies to Improve Interprofessional Advanced Access: A Participatory Action Research Study
Bibliographic record
Abstract
Context: Implementing interprofessional advanced access (AA) requires reorganizing the practices of all team members to improve the timeliness of primary care. Often, team members do not know how to engage in a meaningful change process journey. Evidence-based change strategies that address fundamental care delivery processes could help improve timely access to the appropriate professionals while improving satisfaction of both staff and patients. Objective: To describe foundational evidence-based change strategies used by external facilitators to improve interprofessional AA in primary healthcare (PHC) clinics. Study design and analysis: Participatory action research was used to analyze change strategies and steps required for their implementation. AA indicators (3rd next available appointment, continuity of care, and availability for urgent care) were followed longitudinally to adapt the change strategies and assess their progressive impacts. This process led to the development of change packages. Setting: 8 interprofessional PHC clinics in Quebec, Canada. Population studied: Physicians, nurse practitioners, nurses, social workers and pharmacists. Intervention: External facilitators supported PHC clinics while relying on quality improvement techniques according to the Model for Improvement. Outcomes: Change packages: standardized tools to inform the planning, implementation, and monitoring of change strategies. These include: 1) Steps required to implement the strategy; 2) Key indicators to monitor change and evaluate the strategy’s success; 3) Lessons learned from the process. Results: 4 different change packages were developed. 3 change packages support transformation of appointment systems, including a prioritization algorithm for appointment scheduling, integration of time slots for urgent care, and integration of an online appointment scheduler. One change package aims to improve patient flow and professional autonomy using collective orders. The contents of these change packages, including lessons learnt and impacts on key indicators of AA across participating clinics, will be presented. Conclusions: Improving AA in PHC is complex, but essential change strategies appeared to be common first steps among clinics undertaking this journey. Resulting change packages provide road maps to successfully guide the improvement of AA, lay a strong foundation for further improvement steps, and build a culture of improvement within and among PHC clinics.
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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 | 0.071 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".