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Record W2022480272 · doi:10.5993/ajhb.26.4.1

Using Marketing Research Methods to Evaluate a Stage-Specific Intervention

2002· article· en· W2022480272 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAmerican Journal of Health Behavior · 2002
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsHeart and Stroke Foundation
FundersUniversity of Ottawa
KeywordsMass mediaIntervention (counseling)Health promotionPromotion (chess)Social marketingMarket segmentationControl (management)Direct mailMarketingResource (disambiguation)PsychologyAdvertisingApplied psychologyMedicineMedical educationBusinessComputer scienceNursingPublic healthPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: To show how marketing methods can be used to distribute and evaluate a health promotion intervention. METHODS: Mass media promotion was used to communicate a physical activity resource. Brief telephone interviews were used to screen callers and recruit participants into a controlled trial. Follow-up was conducted 3 months later. RESULTS: Information was gained about the attitudes and motivation of callers. The majority of participants (study and control) made significant changes in their activity levels. CONCLUSION: The study demonstrated that even when mass media channels are used, market segmentation can be achieved and program evaluation conducted.

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.

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)0.023
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.985
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.561
GPT teacher head0.647
Teacher spread0.085 · 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