A Public Health Messaging Campaign to Reduce Caloric Intake: Feedback From Expert Stakeholders
Bibliographic record
Abstract
OBJECTIVE: To obtain expert feedback on a public health messaging campaign to reduce caloric intake in US adults. DESIGN AND SETTING: In 2018, researchers conducted semistructured telephone interviews with US-based experts in obesity prevention, mental health, and health communications. PARTICIPANTS: The research team invited 100 experts to participate using purposive and snowball sampling techniques. Of those invited, 60 completed interviews, among which 37 (62%) were obesity prevention experts, 12 (20%) were mental health experts, and 11 (18%) were health communications experts. MAIN OUTCOME MEASURE: Expert feedback regarding a public health messaging campaign to reduce caloric intake. ANALYSIS: Two researchers reviewed and coded all transcripts. The team identified major themes and summarized findings. RESULTS: Most experts identified barriers to effective calorie reduction including social and environmental factors, lack of actionable strategies, and confusion regarding healthy eating messages. Expert suggestions for effective messaging included addressing eating patterns, emphasizing nutrient density, and dissemination through multiple channels and trusted sources. In general, mental health experts more frequently voiced concerns regarding eating disorders, and communications experts raised issues regarding the dissemination of campaigns. CONCLUSIONS AND IMPLICATIONS: Professionals should identify and address barriers to delivering a calorie reduction campaign before implementation, using strategies that enhance delivery to ensure an effective campaign.
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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 | 0.044 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".