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Advertising Strategies to Increase Public Knowledge of the Warning Signs of Stroke

2003· article· en· W2079593733 on OpenAlexafffundabout
Frank L. Silver, Frank Rubini, Diane Black, Corinne Hodgson

Bibliographic record

VenueStroke · 2003
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsCentre For Cold Ocean Resources EngineeringOntario Stroke NetworkHeart and Stroke Foundation
FundersHealth CanadaHeart and Stroke Foundation of Canada
KeywordsMedicineNewspaperMass mediaAdvertisingStroke (engine)PollingHealth educationWarning signsPublic healthControl (management)Telephone surveyNursingBusiness

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Public awareness of the warning signs of stroke is important. As part of an educational campaign using mass media, the Heart and Stroke Foundation of Ontario conducted public opinion polling in 4 communities to track the level of awareness of the warning signs of stroke and to determine the impact of different media strategies. METHODS: Telephone surveys were conducted among members of the general public in 1 control and 3 test communities before and after mass media campaigns. The main outcome measure used to determine effectiveness of the campaigns was the ability to name > or =2 warning signs of stroke. RESULTS: In communities exposed to television advertising, ability to name the warning signs of stroke increased significantly. There was no significant change in the community receiving print (newspaper) advertising, and the control community experienced a decrease. Television increased the knowledge of both men and women and of people with less than a secondary school education but not of those > or =65 years of age. Intermittent, low-level television advertising was as effective as continuous, high-level television advertising. CONCLUSIONS: Results of this survey can be used to guide mass media-buying strategies for public health education.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.056
GPT teacher head0.419
Teacher spread0.363 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations175
Published2003
Admission routes3
Has abstractyes

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