Factors Affecting Smoking Cessation Efforts of People With Severe Mental Illness: A Qualitative Study
Notice bibliographique
Résumé
OBJECTIVE: People with severe mental illness are much more likely to smoke than are members of the general population. Smoking cessation interventions that combine counseling and medication have been shown to be moderately effective, but quit rates remain low and little is known about the experiences of people with severe mental illness in smoking cessation interventions. To address this gap in knowledge, we conducted a qualitative study to investigate factors that help or hinder the smoking cessation efforts of people with severe mental illness. METHODS: We recruited 16 people with severe mental illness who had participated in a clinical trial of two different smoking cessation interventions, one involving nicotine replacement therapy only and the other nicotine replacement therapy combined with motivational interviewing and a peer support group. We conducted open-ended, semi-structured interviews with participants, who ranged in age from 20 to 56 years old, were equally distributed by gender (eight men and eight women), and were predominantly Caucasian (n = 13, 81%). Primary mental illness diagnoses included schizophrenia/schizoaffective disorder (n = 6, 38%), depression (n = 5, 31%), bipolar disorder (n = 4, 25%), and anxiety disorder (n = 1, 6%). At entry into the clinical trial, participants smoked an average of 22.6 cigarettes per day (SD = 13.0). RESULTS: RESULTS indicated that people with mental illness have a diverse range of experiences in the same smoking cessation intervention. Smoking cessation experiences were influenced by factors related to the intervention itself (such as presence of smoking cessation aids, group supports, and emphasis on individual choice and needs), as well as individual factors (such as mental health, physical health, and substance use), and social-environmental factors (such as difficult life events and social relationships). CONCLUSIONS: An improved understanding of the smoking cessation experiences of people with severe mental illness can inform the delivery of future smoking cessation interventions for this population. The results of this study suggest the importance of smoking cessation interventions that offer a variety of treatment options, incorporating choice and flexibility, so as to be responsive to the evolving needs and preferences of individual clients.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».