Weight Maintenance Through Behaviour Modification: With a Cooking Course or Neurolinguistic Programming
Bibliographic record
Abstract
We compared the effect on weight regain of behaviour modification consisting of either a gourmet cooking course or neurolinguistic programming (NLP) therapy. Fifty-six overweight and obese subjects participated. The first step was a 12-week weight loss program. Participants achieving at least 8% weight loss were randomized to five months of either NLP therapy or a course in gourmet cooking. Follow-up occurred after two and three years. Forty-nine participants lost at least 8% of their initial body weight and were randomized to the next step. The NLP group lost an additional 1.8 kg and the cooking group lost 0.2 kg during the five months of weight maintenance (NS). The dropout rate in the cooking group was 4%, compared with 26% in the NLP group (p=0.04). There was no difference in weight maintenance after two and three years of follow-up. In conclusion, weight loss in overweight and obese participants was maintained equally efficiently with a healthy cooking course or NLP therapy, but the dropout rate was lower during the active cooking treatment.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".