Integrating health geography and behavioral economic principles to strengthen context-specific behavior change interventions
Bibliographic record
Abstract
The long-term economic viability of modern health care systems is uncertain, in part due to costs of health care at the end of life and increasing health care utilization associated with an increasing population prevalence of multiple chronic diseases. Control of health care spending and sustaining delivery of health care services will require strategic investments in prevention to reduce the risk of disease and its complications over an individual's life course. Behavior change interventions aimed at reducing a range of harmful and risky health-related behaviors including smoking, physical inactivity, excess alcohol consumption, and excess weight, are one approach that has proven effective at reducing risk and preventing chronic disease. However, large-scale efforts to reduce population-level chronic diseases are challenging and have not been very successful at reducing the burden of chronic diseases. A new approach is required to identify when, where, and how to intervene to disrupt patterns of behavior associated with high-risk factors using context-specific interventions that can be scaled. This paper introduces the need to integrate theoretical and methodological principles of health geography and behavioral economics as opportunities to strengthen behavior change interventions for the prevention of chronic diseases. We discuss how health geography and behavioral economics can be applied to expand existing behavior change frameworks and how behavior change interventions can be strengthened by characterizing contexts of time and activity space.
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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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".