Real-time recording: A scoping review of methods to study children's real-time exposure to food and food marketing online
Bibliographic record
Abstract
Today, children are exposed to an unprecedented amount of marketing for high-fat, sugar and salt (HFSS) foods and non-alcoholic beverages. Exposure to HFSS products influences children's food preferences and consumption patterns. As children increasingly live, learn and play online, understanding their exposure to food and food marketing in digital environments has become a growing area of research. Real-time recording of children's devices offers an observational method of assessing their exposure to food and food marketing online. This scoping review aimed to identify and analyse studies employing real-time screen recording methods to study children's online exposure to food and food marketing. Three electronic databases (Scopus, Medline, and Web of Science) were used to identify articles published between January 1, 2010, and July 22, 2024. Articles were included if they collected and analysed real-time screen recordings from children (<18 years) using their own devices. Five studies met the criteria, conducted in Australia (n = 2), Mexico (n = 1), and Canada (n = 2). Findings suggest that research on children's exposure to food and food marketing online using real-time recording of their device use is limited. Included studies revealed inconsistencies in data collection, coding, and exposure definitions. A standardised data collection and coding protocol is needed to enhance the comparability and rigour of future research in this field. More high-quality research using real-time recording to assess children's exposure to food and food marketing online is needed. Future research should prioritise the inclusion of participants from low-income countries and diverse socio-economic and ethnic backgrounds to identify potential inequities in children's exposure to food and food marketing online.
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 | 0.052 | 0.158 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.044 | 0.044 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".