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Enregistrement W2072764045 · doi:10.1016/j.pmedr.2015.03.001

Assessing effective smoking cessation intervention in primary care

2015· article· en· W2072764045 sur OpenAlexaffabout
Vladyslav Kushnir, John Cunningham

Notice bibliographique

RevuePreventive Medicine Reports · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueSmoking Behavior and Cessation
Établissements canadiensCentre for Addiction and Mental HealthCanada Research ChairsUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMedicineSmoking cessationNicotine replacement therapyFamily medicineReferralContext (archaeology)Intervention (counseling)Medical prescriptionPopulationPrimary careNursingEnvironmental health

Résumé

récupéré en direct d'OpenAlex

Over the past 20 years, the provision of smoking cessation intervention in primary care has been on the rise. While early reports in late 1980's and 90's have documented that less than 50% of smokers were ever advised to quit (Anda et al., 1987, Goldstein et al., 1997), more recent surveys of both smokers and physicians have revealed that close to 90% of patients are asked of their smoking status and now more than three quarters are advised to quit (AAMC, 2007, King et al., 2013). Evaluating data from the 2009–2010 United States National Adult Tobacco Survey, King et al. recently documented strong provider compliance with the ask and advise components of the 5A's model of physician smoking cessation practice guidelines (Fiore et al., 2008); however, moderate to weak compliance with the assessment, assist and arrangement of follow-up components (King et al., 2013). Of particular note, the study also found that 78.2% of all smokers were offered any assistance and approximately half (49.5%) were provided with 2 or more forms of assistance in the past 12 months, consisting of brief intervention (e.g. booklets, websites), cessation program referral, or medication prescription. Our results from a Canadian population survey conducted in the context of an ongoing trial (study protocol — Cunningham et al., 2011), similarly indicate that 43.3% of adult regular smokers with an intent to quit in the next 6 months (n = 1242) had received brief intervention and nicotine replacement therapy (NRT) or medication, and only 15% had reported receiving both counseling and NRT or medication. While these rates indicate that the provision of some assistance is now more commonplace, offers of combined or alternate lines of support following a failed quit attempt are far from the norm. More importantly however, the above rates are only reflective of smokers being provided with two or more forms of intervention sometime in the past year and do not necessarily speak to the best practice guideline of combined provision of behavioral and pharmacotherapeutic interventions (Fiore et al., 2008, Hurt et al., 1994). In fact, no population or physician surveys to date have reported on the concurrent provision of several smoking cessation interventions. As such, it is striking that population level prevalence rates on the provision of the most effective form of primary care cessation support are simply unknown. Identifying physician compliance with best practice guidelines is necessary and certainly highly encouraged for future population surveys. While the number of received interventions may be telling of physician resourcefulness and persistence in tailoring a treatment plan, the concurrent provision of interventions would be more indicative of physician training and implementation of evidence-based interventions. Documenting the concurrent provision of cessation interventions in particular, is not only important for current indices of physician practices but also for evaluating effectiveness of recent system-wide changes to the provision of tobacco-related interventions in primary care (Kunyk et al., 2014, Land et al., 2012). As more jurisdictions adopt the integrated, multicomponent systems pathway to tobacco treatment, a comprehensive assessment of the types, frequency, duration, as well as combined provision of smoking cessation assistance can help provide a deeper understanding of the gaps and barriers in effective delivery of cessation interventions.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,112
Score d'incertitude au seuil0,599

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,042
Tête enseignante GPT0,367
Écart entre enseignants0,325 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2015
Routes d'admission2
Résumé présentoui

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