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Record W2155831788 · doi:10.1375/jsc.5.1.7

A Qualitative Perspective on Multiple Health Behaviour Change: Views of Smoking Cessation Advisors Who Promote Physical Activity

2010· article· en· W2155831788 on OpenAlexaff
Emma Everson-Hock, Adrian Taylor, Michael Ussher, Guy Faulkner

Bibliographic record

VenueThe Journal of Smoking Cessation · 2010
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSmoking cessationPerspective (graphical)Thematic analysisFeelingFocus groupQualitative researchPsychologyPhysical activityIdentity (music)Behaviour changeApplied psychologyBehavior changeQualitative propertySocial psychologyMedicineIntervention (counseling)SociologyPhysical therapyPsychiatryComputer science

Abstract

fetched live from OpenAlex

Abstract There are mixed views on whether smoking cessation advisors should focus only on quitting smoking or also promote simultaneous health behaviour changes (e.g., diet, physical activity), but no studies have qualitatively examined the views and vicarious experiences of such health professionals. Semi-structured interviews were conducted with 11 trained smoking cessation advisors who promote physical activity to their clients. The data were categorised into themes using thematic analysis supported by qualitative data analysis software. We report themes that were related to why advisors promote multiple health behaviour change and issues in timing. Physical activity could be promoted as a cessation aid and also as part of a holistic lifestyle change consistent with a nonsmoker identity, thereby increasing feelings of control and addressing fear of weight gain. Multiple changes were promoted pre-quit, simultaneously and post-quit, and advisors asserted that it is important to focus on the needs and capabilities of individual clients when deciding how to time multiple changes. Also, suggesting that PA was a useful and easily performed cessation aid rather than a new behaviour (i.e., structured exercise that may seem irrelevant) may help some clients to avoid a sense of overload.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.588
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.109
GPT teacher head0.432
Teacher spread0.322 · 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

Citations29
Published2010
Admission routes1
Has abstractyes

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