The association of motivational factors with weight loss and treatment time in a publicly funded weight management clinic
Bibliographic record
Abstract
The objective of the study is to explore differences in weight loss (WL) and treatment time with having health, mobility, and/or aesthetics motivations for attempting WL. Data from 7540 adults with overweight or obesity who participated in a publicly funded weight management programme at the Wharton Medical Clinic were analysed. Patients' WL motivations were categorised into six groups: Health only; Health and Aesthetics; Health and Mobility; Health, Mobility and Aesthetics; No Health; and None. Women with Health, Mobility and Aesthetics or No Health motivations had marginally higher WL goals compared to other groups, with no differences in men. Men with Health and Aesthetics or Health and Mobility motivations showed marginally higher 6-month attendance rates. Men who discontinued after one visit were 40% less likely to have a Health and Aesthetics motivation as opposed to Health only, compared to those who continued. No differences were observed in WL between motivation groups in either sex. No correlation was found between WL goals and WL attained. Only weak correlations between treatment time and WL were observed across most motivation groups. Despite small differences in treatment time and WL goals, motivations for attempting WL were not significantly associated with differences in the WL achieved.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".