The use of a policy dialogue to facilitate evidence-informed policy development for improved access to care: the case of the Winnipeg Central Intake Service (WCIS)
Bibliographic record
Abstract
BACKGROUND: Policy dialogues are critical for developing responsive, effective, sustainable, evidence-informed policy. Our multidisciplinary team, including researchers, physicians and senior decision-makers, comprehensively evaluated The Winnipeg Central Intake Service, a single-entry model in Winnipeg, Manitoba, to improve patient access to hip/knee replacement surgery. We used the evaluation findings to develop five evidence-informed policy directions to help improve access to scheduled clinical services across Manitoba. Using guiding principles of public participation processes, we hosted a policy roundtable meeting to engage stakeholders and use their input to refine the policy directions. Here, we report on the use and input of a policy roundtable meeting and its role in contributing to the development of evidence-informed policy. METHODS: Our evidence-informed policy directions focused on formal measurement/monitoring of quality, central intake as a preferred model for service delivery, provincial scope, transparent processes/performance indicators, and patient choice of provider. We held a policy roundtable meeting and used outcomes of facilitated discussions to refine these directions. Individuals from our team and six stakeholder groups across Manitoba participated (n = 44), including patients, family physicians, orthopaedic surgeons, surgical office assistants, Winnipeg Central Intake team, and administrators/managers. We developed evaluation forms to assess the meeting process, and collected decision-maker partners' perspectives on the value of the policy roundtable meeting and use of policy directions to improve access to scheduled clinical services after the meeting, and again 15 months later. We analyzed roundtable and evaluation data using thematic analysis to identify key themes. RESULTS: Four key findings emerged. First, participants supported all policy directions, with revisions and key implementation considerations identified. Second, participants felt the policy roundtable meeting achieved its purpose (to engage stakeholders, elicit feedback, refine policy directions). Third, our decision-maker partners' expectations of the policy roundtable meeting were exceeded; they re-affirmed its value and described the refined policy directions as foundational to establishing the vocabulary, vision and framework for improving access to scheduled clinical services in Manitoba. Finally, our adaptation of key design elements was conducive to discussion of issues surrounding access to care. CONCLUSIONS: Our policy roundtable process was an effective tool for acquiring broad input from stakeholders, refining policy directions and forming the necessary consensus starting points to move towards evidence-informed policy.
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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 | 0.181 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.038 | 0.027 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.011 | 0.016 |
| 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".