Déterminants psychosociaux de l'intention de cesser de fumer chez une clientèle soumise à une coronarographie
Bibliographic record
Abstract
RÉSUMÉ : Introduction : Malgré les risques accrus du tabagisme chez les personnes ayant un problème cardiovasculaire, la prévalence de ce comportement demeure préoccupante. En prévention secondaire, la cessation tabagique est essentielle pour améliorer leur pronostic. Comprendre leurs motivations et caractéristiques est un levier crucial pour favoriser l’arrêt du tabac. But : Cette étude visait à expliquer les déterminants de l’intention de cesser de fumer après une coronarographie auprès des personnes fumeuses soumises à une coronarographie. Méthode : 50 usagers fumeurs (M=62,1 ans ; ÉT 10,7) dans un centre tertiaire de cardiologie (Québec, Canada) ont répondu à un questionnaire autoadministré basé sur l’Approche de l’action raisonnée (AAR). Des régressions linéaires multiples ont permis d’identifier les déterminants de l’intention de cesser de fumer ainsi que les croyances les plus fortement associées à cette intention. Résultats : L’intention de cesser de fumer était plutôt favorable (M = 5,4/7 ; ÉT 1,27). Les analyses de régression linéaire révèlent que la perception de contrôle (β = 0,51, p < 0,001) et la norme subjective (β = 0,39, p = 0,02) étaient associées à l’intention. Le modèle complet explique 59 % de la variance de l’intention. Aucune variable sociodémographique, clinique ou comportementale n’était associée à l’intention. La croyance qu’il est possible de cesser de fumer malgré les symptômes de sevrage était la seule fortement associée à l’intention (β = 0,31 ; p = 0,01). Discussion/Conclusion : Ces résultats suggèrent des déterminants pouvant orienter le développement d’interventions pour favoriser l’arrêt tabagique chez cette population. -- Mot(s) clé(s) en français : cessation tabagique, Approche de l’action raisonnée, intention, maladies cardiovasculaires, coronarographie. -- ABSTRACT : Introduction: Despite the increased risks of smoking among people with cardiovascular disease, the prevalence of this behavior remains a concern. In secondary prevention, smoking cessation is essential to improve their prognosis. Understanding their motivations and characteristics is crucial to encouraging them to quit smoking. Objective: This study aimed to explain the determinants of the intention to quit smoking among smokers undergoing coronary angiography. Method: Fifty smoking patients (M = 62.1 years; SD 10.7) in a tertiary cardiology center (Quebec, Canada) completed a self-administered questionnaire based on the Reasoned action approach (RAA). Multiple linear regressions were used to identify the determinants of the intention to quit smoking and the beliefs most strongly associated with this intention. Results: The intention to quit smoking was fairly favorable (M = 5.4/7; SD 1.27). Linear regression analyses revealed that perceived control (β = 0.51, p < 0.001) and subjective norm (β = 0.39, p = 0.02) were associated with intention. The full model explained 59% of the variance in intention. No sociodemographic, clinical, or behavioral variables were associated with intention. The belief that it is possible to quit smoking despite withdrawal symptoms was the only one strongly associated with intention (β = 0.31; p = 0.01). Discussion/Conclusion: These results suggest determinants that could guide the development of interventions to promote smoking cessation in this population. -- Mot(s) clé(s) en anglais : smoking cessation, Reasoned action approach, intention, cardiovascular disease, coronary angiography.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".