Blending Implementation and Improvement Sciences to support successful adoption of integrated care pathways: Social Prescribing for Better Mental Health
Bibliographic record
Abstract
Introduction: Using the trusted process of writing a prescription, and designing a formalized pathway with associated data collection processes, social prescribing (SP) allows providers to refer patients to community-based programs that are co-created according to patients’ interests, goals, and gifts. Context and Target Communities: The Alliance for Healthier Communities’ member network provides interprofessional, integrated and coordinated team-based care focused on supporting clinical and social needs of populations, communities and individuals they serve, in particular they prioritize people who experience barriers to care. Initiative: The SP initiative was designed to fit the organizational and system context of Alliance member organizations, who are engaged in a Learning Health System (LHS) and embedded in the broader context of an Ontario shift towards integrated care, referred to as Ontario Health Teams. Each organization has a unique context and serves distinct populations in diverse geographical communities. The implementation strategy designed for this initiative is guided by principles and values of comprehensive primary health care (CPHC), integrated care and the LHS, and developed with adaptation and co-design in mind, to support fit in different contexts and alignment with the needs of populations served. Its primary aim is: to develop and improve tailored, integrated SP models and ensure sustainable pathways to social and community programs and services. This poster will describe the blended Implementation Science and Quality Improvement (IS/QI) strategy used to enable SP practice transformation. The strategy supports teams from 28 organizations across Ontario, participating in the Alliance’s Social Prescribing for Better Mental Health (SP BMH) initiative, funded by the Public Health Agency of Canada. Approach: The IS/QI strategy incorporates and mobilizes evidence, theories and promising practices from different fields of practice and research. The key objective is to assist teams to develop the capacities and capabilities, (i.e., knowledge, skills and abilities) to engage in practice transformation. Teams are provided with direct implementation and QI coaching support to: assess local context, identify intervention adaptations, develop implementation plans, create process/asset maps, select ongoing QI measures and apply change ideas using Plan Do Study Act cycles. Teams are also provided with opportunities to engage in collaborative peer-learning events. Tools to enable EMR data collection have been co-designed and coaches are assisting teams to: improve data analysis, interpretation and reporting at various stages of the QI cycle, and to use client data to identify program strengths and gaps. Teams are also being supported to learn how to apply LHS principles to their work and use data to plan for sustainability. Next Steps: Early learnings include the importance of dedicated coaching for practice change; how the SP model can broaden reach to build innovative intersectoral community and system partnerships, and how SP enables innovations supporting access to primary care for unattached individuals, as well as provides supports to individuals and groups adversely impacted by determinants of health, such as individuals’ who are precarious housed, or living with serious disabilities. Learnings from the SP BMH initiative are being used to inform a SP spread and scale collaborative.
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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 | 0.037 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".