Promising Practices for the Prevention and Control of Obesity in the Worksite
Bibliographic record
Abstract
Purpose. To identify worksite practices that show promise for promoting employee weight loss. Data Source. The following electronic databases were searched from January 1, 1966, through December 31, 2005: CARL Uncover (via Ingenta), CDP, CINAHL, Cochrane Central Register of Controlled Trials, Cochrane Library, CRISP, Dissertation Abstracts, EMBASE, ERIC, Health Canada, INFORM (part of ABI/INFORM Proquest), LocatorPlus, New York Academy of Medicine, Ovid MEDLINE, SPORTDiscus, PapersFirst, PsycINFO, PubMed, and TRIP. Study Inclusion and Exclusion Criteria. Included studies were published in English, conducted at a worksite, designed for adults (aged ≥18 years), and reported weight-related outcomes. Data Extraction. Data were extracted using an online abstraction form. Data Synthesis. Studies were evaluated on the basis of study design suitability quality of execution, sample size, and effect size. Changes in weight-related outcomes were used to assess effectiveness. Results. The following six promising practices were identified: enhanced access to opportunities for physical activity combined with health education, exercise prescriptions alone, multicomponent educational practices, weight loss competitions and incentives, behavioral practices with incentives, and behavioral practices without incentives. Conclusions. These practices will help employers and employees select programs that show promise for controlling and preventing obesity. (Am J Health Promot 2011;25[3]:e12–e26.)
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".