Effective and resource‐efficient strategies for recruiting families in physical activity, sedentary behavior, nutrition, and obesity prevention research: A systematic review with expert opinion
Bibliographic record
Abstract
We systematically identified effective and resource-efficient strategies for recruiting families into health promoting intervention research. Four databases were searched for reviews. Interventions were extracted from included reviews. Additionally, a Delphi study was conducted with 35 experts in family-based research. We assessed extracted data from our review and Delphi participants' opinions by collating responses into overarching themes based on recruitment setting then recruitment strategies to identify effective and resource-efficient strategies for recruiting families into intervention research. A total of 64 articles (n = 49 studies) were included. Data regarding recruitment duration (33%), target sample size (32%), reach (18%), expressions of interest (33%), and enrollment rate (22%) were scarcely reported. Recruitment settings (84%) and strategies (73%) used were available for most studies. However, the details were vague, particularly regarding who was responsible for recruitment or how recruitment strategies were implemented. The Delphi showed recruitment settings, and strategies fell under six themes: school-based, print/electronic media, community settings-based, primary care-based, employer-based, and referral-based strategies. Underrecruitment in family-based trials is a major issue. Reporting on recruitment can be improved by better adherence to existing guidelines. Our findings suggest a multifaceted recruitment approach targeting adults and children with multiple exposures to study information.
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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 | 0.139 | 0.284 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.039 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".