The <scp>CHANGE</scp> Program: Comparing an Interactive versus Prescriptive Obesity Intervention on University Students' Self‐Esteem and Quality of Life
Bibliographic record
Abstract
BACKGROUND: Previous studies incorporating Motivational Interviewing administered via Co-Active Life Coaching tools (MI-via-CALC) have elicited positive results among adults with obesity. However, there is a paucity of this research that includes sufficient power and a comparison group. This study's purpose was to compare MI-via-CALC with a validated obesity intervention among university students. METHODS: Participants (n = 45) were randomised to either a telephone-based 12-week: (a) MI-via-CALC program whereby a certified coach worked with subjects to achieve goals through dialogue; or (b) lifestyle modification treatment following the LEARN Program for Weight Management. Participants completed the Rosenberg Self-Esteem Scale and Short Form Functional Health Status Scale (SF-36) at baseline, mid-, and post-treatment, and 3 and 6 months following the program. RESULTS: Analyses revealed that both conditions elicited significant time effects between baseline and 6 months for self-esteem and all dimensions of the SF-36 (e.g. overall health). CONCLUSIONS: MI-via-CALC compares favorably with LEARN as an obesity treatment. Given that self-esteem and quality of life are essential for promoting behavior change among individuals with obesity, this study offers unique insights into their change processes. Future research should provide both treatments and allow participants to choose based on their personal preferences, learning styles, and needs.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".