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Record W2110192927 · doi:10.1093/her/cyr043

A media advocacy intervention linking health disparities and food insecurity

2011· article· en· W2110192927 on OpenAlexafffundabout
Melanie Rock, Lynn McIntyre, Steven Persaud, Kristen Thomas

Bibliographic record

VenueHealth Education Research · 2011
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsAlberta InnovatesInstitute of Population and Public HealthUniversity of Calgary
FundersCanadian Institutes of Health ResearchAlberta InnovatesAlberta Heritage Foundation for Medical ResearchFondation pour la Recherche Médicale
KeywordsPopulationPublic healthHealth promotionPublic relationsPovertyHealth communicationPsychological interventionSocial mediaPopulation healthAlternative mediaNews mediaAdvertisingPolitical scienceEnvironmental healthMedicineBusinessNursing

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.604
GPT teacher head0.602
Teacher spread0.003 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2011
Admission routes3
Has abstractyes

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