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Record W1870974973 · doi:10.2196/jmir.4491

“If I Were Nick”: Men’s Responses to an Interactive Video Drama Series to Support Smoking Cessation

2015· article· en· W1870974973 on OpenAlexafffund
Joan L. Bottorff, Gayl Sarbit, John L. Oliffe, Mary T. Kelly, Maria Lohan, Sean Stolp, Paul Sharp

Bibliographic record

VenueJournal of Medical Internet Research · 2015
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNational Institute for Health and Care ResearchBritish Columbia Lung Association
KeywordsSmoking cessationPsychological interventionPsychologyPromotion (chess)DramaHealth promotionMedicineNursingPublic healthVisual arts

Abstract

fetched live from OpenAlex

BACKGROUND: Men continue to smoke in greater numbers than women; however, few interventions have been developed and tested to support men's cessation. Men tend to rely on quitting strategies associated with stereotypical manliness, such as willpower, stoicism, and independence, but they may lack the self-efficacy skills required to sustain a quit. In this paper, we describe the development of and reception to an interactive video drama (IVD) series, composed of 7 brief scenarios, to support and strengthen men's smoking cessation efforts. The value of IVD in health promotion is predicated on the evidence that viewers engage with the material when they are presented characters with whom they can personally identify. The video dramatizes the challenges unfolding in the life of the main character, Nick, on the first day of his quit and models the skills necessary to embark upon a sustainable quit. OBJECTIVE: The objective was to describe men's responses to the If I were Nick IVD series as part of a study of QuitNow Men, an innovative smoking cessation website designed for men. Specific objectives were to explore the resonance of the main character of the IVD series with end-users and explore men's perceptions of the effectiveness of the IVD series for supporting their quit self-management. METHODS: Seven brief IVD scenarios were developed, filmed with a professional actor, and uploaded to a new online smoking cessation website, QuitNow Men. A sample of 117 men who smoked were recruited into the study and provided baseline data prior to access to the QuitNow Men website for a 6-month period. During this time, 47 men chose to view the IVDs. Their responses to questions about the IVDs were collected in online surveys at 3-month and 6-month time points and analyzed using descriptive statistics. RESULTS: The majority of participants indicated they related to the main character, Nick. Participants who "strongly agreed" they could relate to Nick perceived significantly higher levels of support from the IVDs than the "neutral" and "disagree" groups (P<.001, d=2.0, P<.001, d=3.1). The "agree" and "neutral" groups were significantly higher on rated support from the videos than the "disagree" (P<.001, d=2.2, P=.01, d=1.5). Participants' perception of the main character was independent of participant age, education attainment, or previous quit attempts. CONCLUSIONS: The findings suggest that IVD interventions may be an important addition to men's smoking cessation programs. Given that the use of IVD scenarios in health promotion is in its infancy, the positive outcomes from this study signal the potential for IVD and warrant ongoing evaluation in smoking cessation and, more generally, men's health promotion.

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)0.002
metaresearch head score (Gemma)0.007
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0010.001
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 head0.139
GPT teacher head0.482
Teacher spread0.342 · 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

Citations7
Published2015
Admission routes2
Has abstractyes

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