Modeling for personal well-being: time for paradigm change
Bibliographic record
Abstract
The growing demand for health services tied to exploding costs are a growing concern for all national health care systems, which are reaching their limits of volume and case complexities. In the long run reliance on institutional health care delivery systems is not sustainable and may require shifting responsibilities of health management to individuals. Such a shift would require major paradigm change by focusing on individual's well-being and providing tools for its management. For example, by 2015, it is expected that one of every three people worldwide will be overweight and one in ten -- obese. Considering that while preventable, obesity is responsible for over 60% of all leading death causes in developed societies, it is critical to understand the mechanisms that may lead to its control. The obesity model presented in the paper was developed using system dynamics and is based on three-factor models combined into an energy balance equation. The results were tested with weight loss clinic data. Tools for quantified self-tracking may provide an automated source for data collection needed to refine the model and estimated individual characteristics needs as model parameters.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".