Unleashing Synergies and Alliances – Making Sense of the Alphabet Soup
Bibliographic record
Abstract
Introduction: Learn how our Health System Integration team is exploring how to collaborate with and animate various, diverse teams who are eager to build capacity and strengthen Integrated Care. Target: Our HSI mission is to “help Albertans be as healthy, well and independent as they can be in their homes and communities” at a macro and meso system level. An essential element of IC is robust health and social care networks. As such, HSI enables partnerships with academic, health system, social care, community, government and the third sector leaders and teams. Involvement/Engagement: The HSI team has actively collaborated with multiple system leaders and teams supporting the mission. Realizing the complex interaction with health and social care occurs through many modalities (family, clients and family caregivers, neighborhoods, communities, the volunteer sector, municipalities, many provincial and federal government ministries, primary care, and even business) we promote greater integration, and the importance of co-defining, co-design and co-evaluating. What has Happened: HSI collaborates alongside a wide range of partners and teams working towards IC, particularly those that enable community and upstream action on the social determinants of health. With a view to reducing illness and the need for illness care, the team is strategic in facilitating ongoing conversations with multiple local, national, and international partners. This works towards advancing health system wide structures, achievements, networks and conversations focusing on upstream approaches such as integrated cross-settings approaches (Healthier Together), Asset Based Community Development (ABCD), Supporting Family Caregivers, Social Prescribing, Social Determinants of Health (SDOH), Quintuple Aim, Accreditation Canada People-Centred Care Standard, Adverse Childhood Experiences (ACEs), Enhancing Local Decision-Making, and social factors within the province-wide electronic medical record called Connect Care. Results/Impact: Our engagement has been met with robust interest and enthusiasm. Connections have led to other new connections who shared mutual interest and partnerships, joint research initiatives, joint operational initiatives, communities of practice and inform strategy. Importantly, our decision-maker colleagues have also been able to directly enact policy changes such as enhancing transportation opportunities, initiating social prescribing programs, and municipal well-being built and social infrastructure. Significantly, AHS has recently identified health system integration as one of its top five strategic priorities. Learnings: First, relationships have been key in establishing goals and supporting key opinion leaders, influencers, and key organizations. Taking time to agree on a shared vision has proved to be of critical importance. Participation with local AHS Health Zone service planning who have increased focus on SDOH and ABCD, requires deep listening and collaboration with sectors and community. Next Steps: Our goal is to continue IC conversations, alongside other community partners/systems working in this space. We will highlight strategies to collaborate with community, citizens, families and care providers as equal partners using co-design, co-production and co-evaluation principles. We will continue to create space for teams to learn and explore what has worked well and where there are opportunities for improvement.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.029 | 0.046 |
| Scholarly communication | 0.027 | 0.028 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".