Supporting self-management of obesity using a novel game architecture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Obesity has commonly been addressed using a 'one size fits all' approach centred on a combination of diet and exercise. This has not succeeded in halting the obesity epidemic, as two-thirds of American adults are now obese or overweight. Practitioners are increasingly highlighting that one's weight is shaped by myriad factors, suggesting that interventions should be tailored to the specific needs of individuals. Health games have potential to provide such tailored approach. However, they currently tend to focus on communicating and/or reinforcing knowledge, in order to suscitate learning in the participants. We argue that it would be equally, if not more valuable, that games learn from participants using recommender systems. This would allow treatments to be comprehensive, as games can deduce from the participants' behaviour which factors seem to be most relevant to his or her weight and focus on them. We introduce a novel game architecture and discuss its implications on facilitating the self-management of obesity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it