Valuing the Benefits of Weight Loss Programs: An Application of the Discrete Choice Experiment
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
OBJECTIVE: Obesity is a leading health threat. Determination of optimal therapies for long-term weight loss remains a challenge. Evidence suggests that successful weight loss depends on the compliance of weight loss program participants with their weight loss efforts. Despite this, little is known regarding the attributes influencing such compliance. The purpose of this study was to assess, using a discrete choice experiment (DCE), the relative importance of weight loss program attributes to its participants and to express these preferences in terms of their willingness to pay for them. RESEARCH METHODS: A DCE survey explored the following weight loss program attributes in a sample of 165 overweight adults enrolled in community weight loss programs: cost, travel time required to attend, extent of physician involvement (e.g., none, monthly, every 2 weeks), components (e.g., diet, exercise, behavior change) emphasized, and focus (e.g., group, individual). The rate at which participants were willing to trade among attributes and the willingness to pay for different configurations of combined attributes were estimated using regression modeling. RESULTS: All attributes investigated appeared to be statistically significant. The most important unit change was "program components emphasized" (e.g., moving from diet only to diet and exercise). DISCUSSION: The majority of participants were willing to pay for weight loss programs that reflected their preferences. The DCE tool was useful in quantifying and understanding individual preferences in obesity management and provided information that could help to maximize the efficiency of existing weight loss programs or the design of new programs.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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