The<i>Long Live Kids</i>Campaign: Awareness of Campaign Messages
Bibliographic record
Abstract
Media interventions are one strategy used to promote physical activity, but little is known about their effectiveness with children. As part of a larger evaluation, the purpose of this study was to assess the short-term effect of a private industry sponsored media literacy campaign, Long Live Kids, aimed at children in Canada. Specifically, we investigated children's awareness of the campaign and its correlates. Using a cohort design, a national sample (N = 331, male = 171; mean age = 10.81, SD = 0.99) completed a telephone survey two weeks prior to the campaign release, and again 1 year later. Only 3% of the children were able to recall the Long Live Kids campaign unprompted and 57% had prompted recall. Logistic regression found family income (Wald χ(2) = 11.06, p < .05), and free-time physical activity (Wald χ(2) = 5.67, p < .01) significantly predicted campaign awareness. Active children (≥3 days/week) were twice as likely to have recalled the campaign compared with inactive children (<3 days/week), whereas children living in high-income households (>$60,000/yr) were between 3.5 to 5 times more likely to have campaign recall compared with children living in a low-income households (<$20,000/yr). These findings suggest that media campaigns developed by industry may have a role in promoting physical activity to children although our findings identified a knowledge gap between children living in high- and low-income households. Future research needs to examine how children become aware of such media campaigns and how this mediated information is being used by children.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".