Social Marketing for a Healthy Diet: A Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVES: In this study we focused on how social marketing might encourage healthy eating habits, with particular attention to methodological approaches, theoretical frameworks, geographic representations, and important suggestions for further research. BACKGROUND: Social marketing is an important tool for promoting healthy eating, but research has not fully explored the implementation of mixed methodologies, included theory-driven approaches, or represented worldwide geographies. These deficiencies have hampered successful strategy creation. METHOD: We examined 45 research publications in this comprehensive literature review. Methodological approaches, theoretical foundations, intervention tactics, and geographic origins were considered when evaluating the studies. FINDINGS: According to the findings, the study landscape is dominated by the United States and Canada (42%), followed by Europe (31%), with other regions being underrepresented. Despite the potential advantages of mixed-method techniques, just four studies used them. Though most research lacks a theoretical foundation, popular hypotheses include the Technology Acceptance Model and the Theory of Planned Behaviour. One common strategy for encouraging healthier diets is the use of social media. Nevertheless, there is little application of mediating and moderating variables. CONCLUSION: This review emphasises the necessity of theory-driven social marketing initiatives,suggesting the incorporation of sustainability and health into dietary recommendations through strategies such as controlling the promotion of unhealthy foods and utilizing social media tactics like memes, visual stimuli, celebrity endorsements, and peer interactions-all of which are crucial in encouraging consumers, especially women and young audiences, to adopt healthy eating practices.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".