WEIGHTING THE BENEFITS OF WEIGHT LOSS PROGRAMS
Bibliographic record
Abstract
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. PURPOSE To assess the relative importance of weight loss program attributes to its participants, and to express these preferences in terms of their willingness to pay (WTP) for such attributes in a sample of 165 overweight adults enrolled in community weight loss programs using a discrete choice experiment (DCE). METHODS A DCE survey was used to obtain participant preferences for the following attributes of a weight loss program: 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 between attributes and the WTP for different configurations of combined attributes were estimated using regression modeling. RESULTS All attributes investigated appeared to be important. For example, participants were willing to pay \$72.00 more for a level more of physician involvement in their 3-month weight management program. The most important unit change (moving from one attribute level to another) was “program components emphasized” (e.g., moving from diet only to diet and exercise). CONCLUSION The majority of participants were willing to pay for weight loss programs that reflected their preferences. These preferences are important to include in policy-level decision-making. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".