Promising Practices for the Prevention and Control of Obesity in the Worksite
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it