A media advocacy intervention linking health disparities and food insecurity
Bibliographic record
Abstract
Media advocacy is a well-established strategy for transmitting health messages to the public. This paper discusses a media advocacy intervention that raised issues about how the public interprets messages about the negative effects of poverty on population health. In conjunction with the publication of a manuscript illustrating how income-related food insecurity leads to disparities related to the consumption of a popular food product across Canada (namely, Kraft Dinner®), we launched a media intervention intended to appeal to radio, television, print and Internet journalists. All the media coverage conveyed our intended message that food insecurity is a serious population health problem, confirming that message framing, personal narratives and visual imagery are important in persuading media outlets to carry stories about poverty as a determinant of population health. Among politicians and members of the public (through on-line discussions), the coverage provoked on-message as well as off-message reactions. Population health researchers and health promotion practitioners should anticipate mixed reactions to media advocacy interventions, particularly in light of new Internet technologies. Opposition to media stories regarding the socio-economic determinants of population health can provide new insights into how we might overcome challenges in translating evidence into preventive interventions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".