Getting to Know the Competition: A Content Analysis of Publicly and Corporate Funded Physical Activity Advertisements
Bibliographic record
Abstract
The purpose of this research was to conduct a content analysis of physical activity advertisements in an effort to determine which advertisements were more likely to include features that may attract and maintain attention levels. Fifty-seven advertisements were collected from top circulation Canadian magazines. The advertisements ranged from publicly funded health promotion pieces to corporate sponsored advertisements using physical activity to sell a product. Advertisements were examined for textual and pictorial factors thought to increase attention allocated to advertising of this nature. Only two public health advertisements were found, and the majority of advertisements (57.9%) were from commercial advertisers using physical activity images to sell products or to encourage brand recognition. The advertisements originating with the private sector tended to possess most of the characteristics thought to attract the attention of readers. Once this attention was gained, however, most of these advertisements failed to highlight the benefits of physical activity. As a result, the positive effect of these advertisements may have been compromised. Public health advertisements were so infrequent that we could not compare their characteristics with those originating with the private sector. The characteristics with those we did find were inconsistent with those thought to attract and maintain attention levels. Results are discussed in terms of potential implications for promoting physical activity.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".