Promoting Evidence-Based Tobacco Cessation Treatment in Community Mental Health Clinics: Protocol for a Prepost Intervention Study
Notice bibliographique
Résumé
BACKGROUND: Tobacco smoking is highly prevalent among persons with serious mental illness (SMI) and is the largest contributor to premature mortality in this population. Evidence-based smoking cessation therapy with medications and behavioral counseling is effective for persons with SMI, but few receive this treatment. Mental health providers have extensive experience working with clients with SMI and frequent treatment contacts, making them well positioned to deliver smoking cessation treatment. However, few mental health providers feel adequately trained to deliver this treatment, and many providers believe that smokers with SMI are not interested in quitting or have concerns about the safety of smoking cessation pharmacotherapy, despite substantial evidence to the contrary. OBJECTIVE: We present the protocol for the pilot "IMPACT" (Implementing Action for Tobacco Smoking Cessation Treatment) study, which aims to pilot test a multicomponent implementation intervention to increase the delivery of evidence-based tobacco smoking cessation treatment in community mental health clinics. METHODS: We are using a prepost observational design to examine the effects of an implementation intervention designed to improve mental health providers' delivery of the following four evidence-based practices related to smoking cessation treatment: (1) assessment of smoking status, (2) assessment of willingness to quit, (3) behavioral counseling, and (4) pharmacotherapy prescribing. To overcome key barriers related to providers' knowledge and self-efficacy of smoking cessation treatment, the study will leverage implementation strategies including (1) real-time and web-based training for mental health providers about evidence-based smoking cessation treatment and motivational interviewing, including an avatar practice module; (2) a tobacco smoking treatment protocol; (3) expert consultation; (4) coaching; and (5) organizational strategy meetings. We will use surveys and in-depth interviews to assess the implementation intervention's effects on providers' knowledge and self-efficacy, the mechanisms of change targeted by the intervention, as well as providers' perceptions of the acceptability, appropriateness, and feasibility of both the evidence-based practices and implementation strategies. We will use data on care delivery to assess providers' implementation of evidence-based smoking cessation practices. RESULTS: The IMPACT study is being conducted at 5 clinic sites. More than 50 providers have been enrolled, exceeding our recruitment target. The study is ongoing. CONCLUSIONS: In order for persons with SMI to realize the benefits of smoking cessation treatment, it is important for clinicians to implement evidence-based practices successfully. This pilot study will result in a set of training modules, implementation tools, and resources for clinicians working in community mental health clinics to address tobacco smoking with their clients. Trial Registration: ClinicalTrials.gov NCT04796961; https://clinicaltrials.gov/ct2/show/NCT04796961. TRIAL REGISTRATION: ClinicalTrials.gov NCT04796961; https://clinicaltrials.gov/ct2/show/NCT04796961. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44787.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,052 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,004 |
| Méta-épidémiologie (sens large) | 0,007 | 0,006 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,007 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,086 | 0,017 |
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 source (Gemma direct ou Codex distillé), 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 ».