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Record W2338035804 · doi:10.1177/1524500415620138

Application of the Transtheoretical Model and Social Marketing to Antidepression Campaign Websites

2015· article· en· W2338035804 on OpenAlexaff
T Levit, Magdalena Cismaru, Alexis Zederayko

Bibliographic record

VenueSocial Marketing Quarterly · 2015
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsTranstheoretical modelPromotion (chess)Context (archaeology)Social marketingMental healthPsychological interventionSocial mediaRelevance (law)Set (abstract data type)MarketingProduct (mathematics)Behavior changeHealth promotionPsychologyMedicinePublic relationsAdvertisingBusinessPsychotherapistSocial psychologyNursingPolitical scienceComputer sciencePublic healthWorld Wide Web

Abstract

fetched live from OpenAlex

Antidepression social marketing (SM) campaigns are often accompanied by comprehensive websites to engage and help people with mental health problems; however, the use of theory is not always apparent. The transtheoretical model (TTM) involves five stages of change through which a person transitions to a healthier life. This paper combines TTM and the fundamental principles of SM, such as consumer orientation, targeting, and value creation and exchange through 4Ps (product, place, promotion, and price), and applies them to mental e-health. We create a set of detailed criteria to guide the development of antidepression websites. These criteria are further used to analyze the online content of five antidepression campaigns and to demonstrate the relevance of TTM and SM to online delivery of the information and self-help interventions in the context of depression.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.011
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.002
Science and technology studies0.0020.006
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.025
GPT teacher head0.344
Teacher spread0.319 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2015
Admission routes1
Has abstractyes

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