Health-promoting skills for children: Evaluating the influence of a media literacy and food marketing intervention
Bibliographic record
Abstract
Background: Media literacy skills are needed to navigate high levels of food marketing promoting the consumption of unhealthy foods. Health-promoting media literacy education encourages children to use analytical skills to critically examine media messages in order to make informed health choices. Objective: To evaluate the influence of media literacy lesson plans for children focusing on critical knowledge around food marketing. Design: Evidence-based Media Literacy & Food Marketing lesson plans, designed for grades 3 to 6 (ages 8–11) and 6 to 9 (ages 11–14), were developed to fill the knowledge gaps children demonstrated with respect to assessing of the healthfulness of packaged foods. Setting: Two public schools in Calgary, Alberta, Canada. Methods: An educational intervention with pre-test/post-test design. The lesson plans were used by teachers in the classroom, and a questionnaire was created to assess children’s pre- and post-lesson levels of critical knowledge about food marketing. Results: In total, 71 students from grades 5, 7, 8 and 9 participated. Qualitative analysis of responses showed increased analysis and evaluation skills when it came to understanding of food marketing appeals, and increased ability to assess the nutritional content of packaged foods. Conclusion: This study is novel in its use of media literacy as a framework for understanding food packaging appeals. It highlights the importance of examining procedural and interpretive knowledge in the evaluation of critical media literacy skills around food. This allows researchers, educators and health practitioners to better gauge how children are able to apply nutrition information in different contexts to make informed food choices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".