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Record W4315786483 · doi:10.3390/ijerph20021397

Fact or Fiction? The Development and Evaluation of a Tobacco Virtual Health Tool

2023· article· en· W4315786483 on OpenAlexafffund
Geneviève Jessiman‐Perreault, Rachel Dunn, Angela Erza, Candace Kratchmer, Ameera Memon, Howie Thomson, Lisa Allen Scott

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of CalgaryAlberta Health Services
FundersAlberta HealthCanadian Patient Safety Institute
KeywordsTobacco useEnvironmental healthComputer scienceMedicine

Abstract

fetched live from OpenAlex

The virtual setting is an important setting for health promotion as individuals increasingly go online for health information and support. Yet, users can have difficulty finding valid, trustworthy, and user-friendly health information online. In 2022, we launched an interactive Fact or Fiction Tobacco virtual health tool. The virtual health tool uses evidence-informed tailored content to engage users and refer them to local tobacco cessation resources. The present paper describes the development, user testing, and evaluation of this tool. The Fact or Fiction virtual health tool was designed by tobacco cessation and health marketing experts and informed by health behaviour theories of change. The tool captures data on who is seeking health information, the user's stage of readiness to quit tobacco products, and whether they act by accessing referred resources. In 2021, we conducted two phases of user testing prior to marketing the tool publicly. After 7 weeks of marketing, we collected data on user interactions with the tool and evaluated the reach of the tool. Results from user testing found the tool to be engaging, easy to use, and quick to complete. Adaptations were made to simplify and condense text and include additional animations. During the first seven weeks of the tool being live, it reached 2306 users, and 38.7% of those users were current or occasional tobacco users. Users were classified based on their intention to quit. Bivariate analysis found that the tool was successful in driving tobacco users towards action as 21.2% tobacco users who were looking to quit and 8.8% of tobacco users who were not looking to quit clicked on local tobacco cessation resources. This virtual health tool is reaching the targeted population and providing tailored information needed at each stage of the continuum of health behaviour change. Among tobacco users looking to quit, this virtual health tool acts as a quick referral to local tobacco cessation resources.

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)0.047
metaresearch head score (Gemma)0.097
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.097
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.292
GPT teacher head0.549
Teacher spread0.257 · 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 designBench or experimental
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

Citations1
Published2023
Admission routes2
Has abstractyes

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