Impact of Weight of the Nation Community Screenings on Obesity-Related Beliefs
Bibliographic record
Abstract
INTRODUCTION: HBO's Weight of the Nation was a collaborative effort among several national organizations to raise awareness about the complexity of the obesity epidemic and promote action through media and community forums. The primary aim of this study was to assess the short-term effects of Weight of the Nation community screenings on obesity-related beliefs, intentions, and policy support. METHODS: Five Prevention Research Centers across the U.S. administered surveys at nine Weight of the Nation community screenings between September 2012 and May 2013. Adults aged ≥18 years who completed pre-post surveys were included. The survey assessed demographic information, perceptions of the documentary, efficacy to take action and influence policies that affect obesity, intentions to take actions to support a healthy weight, and positions on policy changes that impact food systems. Data were analyzed in 2015. RESULTS: A convenience sample of 442 individuals completed surveys. The sample was mostly health workers, female, college educated, aged 25-44 years, and racially and ethnically diverse. Significant increases (p<0.001) were observed for perceived self- and collective efficacy that individuals and communities can influence policies and environmental factors that affect obesity, intentions to take actions that contribute to a healthy weight, and support for policies that change the food system. CONCLUSIONS: A broad, nationwide effort, such as Weight of the Nation, that combines media with opportunities to bring community members together for discussion, may play a role in influencing beliefs, intentions, and policy support regarding obesity prevention.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".