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Record W2385757068 · doi:10.4137/sart.s34551

Blogging to Quit Smoking: Sharing Stories from Women of Childbearing Years in Ontario

2016· article· en· W2385757068 on OpenAlexaffabout
Nadia Minian, Aliya Noormohamed, Rosa Dragonetti, Julie E. Maher, Christina Lessels, Peter Selby

Bibliographic record

VenueSubstance Abuse Research and Treatment · 2016
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsOntario Tobacco Research UnitUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsJournaling file systemQuit smokingSmoking cessationMedicineQualitative researchSocial mediaFamily medicinePsychology

Abstract

fetched live from OpenAlex

This study examined the degree to which the pregnant or postpartum women, in the process of quitting smoking, felt that writing in a blog about their smoking cessation journeys helped them in their efforts to become or remain smoke free. Five women who blogged for Prevention of Gestational and Neonatal Exposure to Tobacco Smoke (a website designed to help pregnant and postpartum women quit smoking) were interviewed about their experiences as bloggers. Participants were asked to complete an online survey, which had closed-ended questions regarding their sociodemographic and smoking characteristics. Once they completed the survey, semistructured qualitative interviews were conducted over the phone. Findings suggest that blogging might combine several evidence-based behavioral strategies for tobacco cessation, such as journaling and getting support from others who use tobacco. Being part of a blogging community of women who have experienced or are experiencing similar challenges can be therapeutic and help women gain confidence in their ability to quit smoking. In conclusion, blogging may help pregnant and postpartum women quit smoking by increasing their social support and promoting self-reflection.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.977

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 head0.102
GPT teacher head0.354
Teacher spread0.251 · 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 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

Citations2
Published2016
Admission routes2
Has abstractyes

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