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Fathers’ narratives of reducing and quitting smoking

2008· article· en· W1973285021 on OpenAlexaff
Joan L. Bottorff, Jenny Radsma, Mary T. Kelly, John L. Oliffe

Bibliographic record

VenueSociology of Health & Illness · 2008
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsNarrativeSmoking cessationPsychological interventionAutonomyPsychologyHarm reductionFeelingDevelopmental psychologySocial psychologyMedicinePsychiatryNursingPolitical sciencePublic health

Abstract

fetched live from OpenAlex

Despite much attention on women's efforts to reduce or stop smoking during pregnancy and postpartum, less attention has been directed to fathers' experiences in modifying their smoking. Using narrative methods, interviews with 29 new fathers were examined to identify different ways in which men approached reducing or quitting smoking. Four storylines were identified: the cold turkey storyline framed quitting smoking as a snap decision with no need for support or smoking cessation aids; the planned reduction storyline focused on building up reasons to quit and developing detailed strategies to enhance the likelihood of success; the baby as the patch storyline dramatised how the baby displaced the need to smoke, increased motivation for cessation and enhanced success; and, finally, a story of forced reduction that highlighted difficulties with smoking cessation for a highly addicted smoker and the tension and conflict this created in his relationship with his partner. Common to all the storylines was the men's reluctance to rely on smoking cessation resources; instead, self-reliance, willpower, and autonomy figured more prominently in their narratives. The findings from this study support developing gender-sensitive tobacco reduction interventions for fathers who smoke.

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)0.001
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 score0.184
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.059
GPT teacher head0.355
Teacher spread0.297 · 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

Citations54
Published2008
Admission routes1
Has abstractyes

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