Creating Health and Wellbeing through Integrated Care
Bibliographic record
Abstract
The International Foundation for Integrated Care Canada (IFIC Canada) will be hosting the second North American Conference on Integrated Care in Canada in October 2024 (NACIC24). The goal for the conference is to advance the knowledge and capability around four strategic themes to advance health and wellbeing through the advancement of integrated care. In this 90-minute workshop, we will explore and co-design the themes for the NACIC24 conference. We adopt a world café format focused on four themes that exemplify effective approaches in advancing integrated care that lead to health creation in our communities. The primary strategic themes encompass the 9 Pillars of Integrated Care: Explore Human-Level Transformation for Collective Impact, emphasizing collective action and power dynamics and including Integrated care Pillars of shared values and vision and system wide governance and leadership. The complexities of Building Emergent Care Teams, encompassing roles, workforce dynamics, and digital health applications and including integrated care Pillars relating to workforce capacity and capability, digital solutions and aligned payment systems. Health Creation, Asset-Based Community Development, Realizing Population Health, and Indigenous Ways of Knowing. These relate to the Integrated care Pillars of population needs and local context, people as patners in health and care, and resilient communities and new alliances. Across all three themes, we aim to examine Evaluation methodologies, including the assessment of relationships, trust, and the collective impact of integrated care initiatives, relating to transparency of progress, results and impact of integrated care. The 90 workshop will begin with a 10 minute introduction to the facilitators, the themes and the structure of the workshop. There will be three simultaneous world-café discussions discussing the best focus for organizing papers and sessions relating to each of the primary themes for 15 minutes each. Each discussion will then add 5 minutes to specifically discuss the possibilities for measurement and evaluation in each of the three themes leaving 20 minutes for table reports. Participants will transition through topics with topic-specific facilitators moving between tables. The outcomes of this workshop will inform the design of NACIC24 conference and serve as the basis for a policy brief on ‘Current issues in Integrated Care for Health Creation’ which will be shared with all participants to leverage in their own countries and communities.
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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 | 0.022 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".