Recruiting a geographically, ethnically and socioeconomically diverse population of postpartum women into a behavioural weight management intervention trial: an evaluation of strategies used in the Supporting MumS (SMS) randomised controlled trial.
Bibliographic record
Abstract
Objective To evaluate strategies used to recruit a geographically, ethnically and socioeconomically diverse sample of postpartum women (including those who gave birth but who do not identify as women) to the Supporting MumS randomised controlled trial (RCT). Design Two-arm, parallel, multi-centre RCT. Setting Five sites across the United Kingdom, selected with consideration of geographic, ethnic and socioeconomic diversity. Population or Sample Women, within 6 weeks to two years postpartum, with BMI ≥25 kg/m 2 . Methods Strategies used to recruit over a 12-month period were recorded, and outcomes were assessed by site, ethnicity and Index of Multiple Deprivation (IMD) data. Main Outcome Measures Numbers expressing interest, screened and randomised. Results Over 12 months, 2,457 women expressed an interest, 1,227 (49.9%) were screened for eligibility and 892 (36.3%) were randomised. The sample had 33.6% non-white ethnicity and 53.5% lived in the two most deprived quintiles (according to IMD). Most participants learnt about the study through social media (45.5%), followed by community groups and settings aimed at parents (26.0%), the Born in Bradford’s Better Start ( BiBBS ) cohort (12.5%), friends/family (7.2%), general community settings (5.3%) and health professionals (5.4%). Success of recruitment strategies varied across sites and participant characteristics. Conclusions An ethnically and socioeconomically diverse sample of postpartum women across the four UK countries was recruited within the planned 12-month timeframe. This was achieved by selection of recruitment sites (considering geographical spread and population characteristics), using a range of strategies and tailoring these to local populations, and iteratively adapting strategies employed based on success.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".