Healthier Together: co-creating healthy and well communities through intersectoral partnerships.
Bibliographic record
Abstract
Background and rationale: Over 60% of health is shaped by the places where we spend our time, by our relationships, and the circumstances in which we live, work, learn, play, and age. Creating healthy environments with communities, workplaces, schools, and health care settings is one of the best ways to keep people healthy and well where we live, learn, work, play, and heal. For more than a decade, Alberta Health Services (AHS), in Alberta, Canada has implemented Healthier Together projects with partners across the province but has needed to focus on implementation and impact within single settings. To realize improvements in local health and wellbeing priorities, there is a need to integrate action across settings within communities through novel approaches that foster local decision making, collaboration and partnership. Who is it for? Healthier Together is a population health approach designed and adaptable to meet various communities' needs. From small rural communities to large urban centers the asset-based community development approach and population health principles are designed to impact the health and wellbeing of whole populations. Currently, we are engaging with 7 diverse communities across the province of Alberta with the plan to scale-up Healthier Together in 2024 and beyond. Who did you involve and engage with: At the Systems-level Healthier Together breaks down silos across the health system (public health, primary care, acute care, chronic disease prevention, data, and analytics, etc.) through a connected governance structure that has implementation, research, evaluation, analytics, communication, and engagement support at its core. At the local level, Healthier Together creates opportunities for intersectoral partners in health, education, employment, municipal government, social services, and citizens within diverse communities to work together across the pillars of integrated care. What did you do? Partners from health care, municipal governments, community organizations, schools, workplaces, and citizens come together to co-create a common vision, use asset-based approaches to understand the community context, create an evidence-informed action plan that responds to local priorities and acts on the determinants of health, collaboratively implement and evaluate, and sustain what works. Healthier Together Initiatives are locally led and supported by system-wide governance and leadership and processes that promote collaboration, partnership, and local effectiveness. Emerging local priorities include mental health, homelessness, and healthy youth and families. What results and impact did you have? Engagement within the health system and with partners on Healthier Together as a way of working has been received with great interest. There is a need to continually build relationships and trust to successfully work across systems at the community-level. Primary outcomes measures and stories of impact are centered on engagement, partnership and trust building in years 1-2 of Healthier Together. A common outcomes framework for measuring improved outcomes in supportive environments, protective and risk factors and health and wellbeing outcomes is under development and will be used to measure progress and results. Healthier Together has the potential to be a model way of working for health systems and communities and learnings will be applicable across jurisdictions.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.016 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.050 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".