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Record W2809434777 · doi:10.33423/jabe.v20i1.320

Using Social Media to Change Smoking Behavior: Line Instant Messaging Application Perspectives

2018· article· en· W2809434777 on OpenAlexvenueno aff
Ratchanee Kulsolkookiet, Nitaya Wongpinunwatana, Lovepon Savaraj, Thunyanee Pothisarn

Bibliographic record

VenueJournal of Applied Business and Economics · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsnot available
Fundersnot available
KeywordsSmoking cessationInstant messagingPsychologySocial psychologyGraduate studentsQuit smokingControl (management)Clinical psychologyApplied psychologyMedicineComputer science

Abstract

fetched live from OpenAlex

The objective of this study is to examine the influence of anti-smoking multimedia on smoking-cessation behavior with respect to attitude towards smoking, belief in subjective norms of smoking, perceived behavioral control of smoking, intentions to reduce smoking, and motivation for smoking cessation. The study is based on an experimental research. The experimental design consisted of three types of multimedia anti-smoking campaigns (text, text with pictures, and video), and three levels of human caring (environment, surrounding people, and themselves). A total of 90 graduate and undergraduate students participated in the experiment. The findings suggested that participants (1) have unfavorable attitudes towards smoking, (2) are indifferent to belief in subjective norms of smoking, and (3) possess greater perceived behavioral control of smoking after viewing the anti-smoking multimedia campaign for the three levels of human caring. Furthermore, the research found that perceived behavioral control of smoking indirectly influences smoking cessation behavior via intention to reduce smoking. Finally, motivation for smoking cessation directly affects smoking cessation behavior.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

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

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 head0.129
GPT teacher head0.302
Teacher spread0.174 · 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 designQualitative
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

Citations0
Published2018
Admission routes1
Has abstractyes

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