Gender differences in response to an opportunistic brief intervention for obesity in primary care: Data from the <scp>BWeL</scp> trial
Bibliographic record
Abstract
Weight loss programmes appeal mainly to women, prompting calls for gender-specific programmes. In the United Kingdom, general practitioners (GPs) refer nine times as many women as men to community weight loss programmes. GPs endorsement and offering programmes systematically could reduce this imbalance. In this trial, consecutively attending patients in primary care with obesity were invited and 1882 were enrolled and randomized to one of two opportunistic 30-second interventions to support weight loss given by GPs in consultations unrelated to weight. In the support arm, clinicians endorsed and offered referral to a weight loss programme and, in the advice arm, advised that weight loss would improve health. Generalized linear mixed effects models examined whether gender moderated the intervention. Men took effective weight loss action less often in both arms (support: 41.6% vs 60.7%; advice: 12.1% vs 18.3%; odds ratio (OR) = 0.38, 95% confidence interval (CI), 0.27, 0.52, P < .001) but there was no evidence that the relative effect differed by gender (interaction P = .32). In the support arm, men accepted referral and attended referral less often, 69.3% vs 82.4%; OR = 0.48, 95% CI, 0.35, 0.66, P < .001 and 30.4% vs 47.6%; OR = 0.48, 95% CI, 0.36, 0.63, P < .001, respectively. Nevertheless, the gender balance in attending weight loss programmes closed to 1.6:1. Men and women attended the same number of sessions (9.7 vs 9.1 sessions, P = .16) and there was no evidence weight loss differed by gender (6.05 kg men vs 4.37 kg women, P = .39). Clinician-delivered opportunistic 30-second interventions benefits men and women equally and reduce most of the gender imbalance in attending weight loss programmes.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".