Development of a population-based microsimulation mode of physical activity in Canada.
Bibliographic record
Abstract
BACKGROUND: Computer simulation modeling makes it possible to project physical activity levels and the prevalence of related health outcomes. Such projections can help to inform programs that aim to increase physical activity levels and improve population health. DATA AND METHODS: The Population Health Model (POHEM) platform was used to develop a dynamic microsimulation model of physical activity among Canadian adults. Key parameters were derived from the National Population Health Survey (1994/1995 to 2006/2007) and the 2000/2001 Canadian Community Health Survey. To assess the validity of the physical activity module (POHEM-PA), estimates from the simulation projections were compared with results from nationally representative surveys. RESULTS: Trends over time in physical activity levels, chronic disease prevalence, and Health Utilities Index based on POHEM-PA projections were similar to those based on data from subsequent cycles of the Canadian Community Health Survey. INTERPRETATION: The addition of a physical activity module to POHEM provides a tool that can improve understanding of the complex dynamics underlying the relationship between physical activity and health outcomes as a population ages.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".