Application of the Health Belief Model to U.S. Magazine Text and Image Coverage of Skin Cancer and Recreational Tanning (2000–2012)
Bibliographic record
Abstract
The health belief model (HBM) has been widely used to inform health education, social marketing, and health communication campaigns. Although the HBM can explain and predict an individual's willingness to engage in positive health behaviors, its application to, and penetration of the underlying constructs into, mass media content has not been well characterized. We examined 574 articles and 905 images about skin cancer and tanning risks, behaviors, and screening from 20 U.S. women's and men's magazines (2000-2012) for the presence of HBM constructs: perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy, and cues to action. Susceptibility (48.1%) and severity (60.3%) information was common in text. Perceived benefits (36.4%) and barriers (41.5%) to prevention of skin cancer were fairly equally mentioned in articles. Self-efficacy (48.4%) focused on sunscreen use. There was little emphasis on HBM constructs related to early detection. Few explicit cues to action about skin cancer appeared in text (12.0%) or images (0.1%). HBM constructs were present to a significantly greater extent in text versus images (e.g., severity, 60.3% vs. 11.3%, respectively, χ(2) = 399.51, p < .0001; benefits prevention, 36.4% vs. 8.0%, respectively, χ(2) = 184.80, p < .0001), suggesting that readers are not visually messaged in ways that would effectively promote skin cancer prevention and early detection behaviors.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".