Social marketing including financial incentive programs at worksite cafeterias for preventing obesity: a systematic review
Bibliographic record
Abstract
BACKGROUND: As with food-taxation strategies, such interventions as discounted healthy menus, point-of-purchase advertisements, and sugar-free beverages for employees at worksites could help prevent obesity. This study assessed the effectiveness of food environment interventions incorporating financial incentive or social marketing strategies at workplace cafeterias, vending machines, and kiosks toward preventing obesity and improving dietary habits. METHODS: We conducted searches on CENTRAL, MEDLINE, EMBASE, CINAHL, and PsycINFO databases. The study designs included were randomized control trials (RCTs) and cluster RCTs. We evaluated the effectiveness of financial incentive or social marketing strategies interventions (such as discounts) on health outcomes or food intake behavior. Two reviewers independently screened the studies for inclusion. We assessed the risk of bias using the Cochrane Collaboration's tool. This protocol was published in 2014. RESULTS: We included three trials, with a combined total of 3013 participants. There were limited available data from RCTs on changes in body weight. No eligible social marketing studies were retrieved. In some cases, a meta-analysis could not be conducted owing to differences in the analytic methods for the outcomes. CONCLUSIONS: Lack of evidence made it difficult to draw any conclusions. In future surveys, it will be necessary to conduct interventions focusing only on financial incentive intervention versus no intervention in order to determine whether the incentive strategy has a clear impact. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD4201401056.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".