Leveraging Artificial Intelligence and Participatory Modeling to Support Paradigm Shifts in Public Health: An Application to Obesity and Evidence-Based Policymaking
Bibliographic record
Abstract
The Provincial Health Services Authority (PHSA) of British Columbia suggested that a paradigm shift from weight to well-being could address the unintended consequences of focusing on obesity and improve the outcomes of efforts to address the challenges facing both individuals and our healthcare system. In this paper, we jointly used artificial intelligence (AI) and participatory modeling to examine the possible consequences of this paradigm shift. Specifically, we created a conceptual map with 19 experts to understand how obesity and physical and mental well-being connect to each other and other factors. Three analyses were performed. First, we analyzed the factors that directly connect to obesity and well-being, both in terms of causes and consequences. Second, we created a reduced version of the map and examined the connections between categories of factors (e.g., food production, and physiology). Third, we explored the themes in the interviews when discussing either well-being or obesity. Our results show that obesity was viewed from a medical perspective as a problem, whereas well-being led to broad and diverse solution-oriented themes. In particular, we found that taking a well-being perspective can be more comprehensive without losing the relevance of the physiological aspects that an obesity-centric perspective focuses on.
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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 | 0.083 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".