What, when, and how food and beverage are advertised on Ghanian television
Bibliographic record
Abstract
Food marketing has increased volume, precision, and reach to influence viewers' food attitudes, beliefs, and eating behaviors. What and how much people eat has implications for health. While many countries regulate food advertising to protect consumers and encourage healthy eating, Ghana has none. Understanding the content and framing of food and beverage advertisements can inform the development of effective policies and practices that encourage healthier diets. This content analysis examines the foods and beverages advertised, their timing, and marketing techniques on Ghanaian television. From February to May 2020, 486 hours of advertisements were recorded. Advertisements with ≥1 actors were coded for food type, actor characteristics (i.e., body size, gender, age, race), and marketing techniques (i.e., promotional characters, premium offers, goal frames). A total of 607 advertisements with 2,043 actors were analyzed. Two-thirds (66.8%) promoted foods categorized as unhealthy. Sugar-sweetened beverages (22.6%) were most frequent, followed by grains high in sugar and low in fiber (13.2%), recipe additions (13.1%), and supplements (10.2%). Half (52.9%) of advertisements used persuasive marketing strategies. Most actors were classified as underweight (72.1% v. 20.5% normal weight, 7.4% overweight/obese) with a balanced gender distribution (49.1% female). Most advertisements aired during evenings (37.7%) and weekdays (69.5%). Morning advertisements promoted more healthy foods, whereas evening and night advertisements promoted more unhealthy foods. Gain goal frames were most common for healthy foods (p < 0.001), hedonic frames for unhealthy foods (p < 0.001), and normative frames showed no difference (p = 0.54). Underweight actors frequently appeared in unhealthy advertisements (68.3% v. 56.0% normal weight, 59.0% overweight/obese), whereas normal-weight (44.0%) and overweight/obese actors (41.0% v. 31.7% underweight) appeared in healthy advertisements. Persuasive marketing strategies were frequently advertised with unhealthy foods (59.9%) and overweight/obese (54.9%) and male actors (53.6%). This study highlights the need for effective policies to regulate food marketing, promoting healthier diets and realistic body expectations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".