Navigating Health and Social Complexity: A Transformational Framework for Adaptation and Change
Bibliographic record
Abstract
In the rapidly evolving landscape of health and social complexity, the need for effective and adaptable models of transformation is paramount. This presentation introduces a framework, derived from non-systematic, non-structured research, that identifies six key clusters of elements of transformation. These clusters serve as a compass for organizations and individuals navigating the complex terrain of change within the realm of health and social complexity.The growing prevalence of chronic diseases is one of the strongest drivers of this health and social complexity. In Canada, for instance, 4 out of 0 people have at least one chronic disease, according to the Public Health Agency of Canada. These conditions consume additional societal resources, leading to increased companionship needs, dependency, and fragility. They also exacerbate health inequalities. The unemployment rate for females with diabetes is almost 2.5 times higher than that of the control group (Robinson, 989). Persons with diabetes have poorer labor outcomes in terms of length of unemployment and lower income (Rodriguez-Sanchez,207). Housing insecurity influences diabetes processes of care and self-care behaviors, and this relationship varies by employment status and race/ethnicity (Mosley-Johnson, 2022).The findings for the framework are model-agnostic, meaning its applicability extends beyond specific transformation models within a system. It provides a universal language for discussing transformation, thereby fostering more effective communication and collaboration, while helping set the vision and journey among multi-disciplinary teams.The presentation will provide an opportunity for participants to share and reflect on their current state of transformation. Through a process of self-assessment, participants will identify their strengths within the six clusters. This introspective exercise will empower participants to anticipate and accelerate their ongoing efforts in care transformation, thereby enhancing their ability to foster their speed to do things while keeping the scope of being person-centric.
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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 | 0.028 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.017 | 0.109 |
| Scholarly communication | 0.029 | 0.025 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".