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

Assessing effective smoking cessation intervention in primary care

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

Bibliographic record

VenuePreventive Medicine Reports · 2015
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsCentre for Addiction and Mental HealthCanada Research ChairsUniversity of Toronto
Fundersnot available
KeywordsMedicineSmoking cessationNicotine replacement therapyFamily medicineReferralContext (archaeology)Intervention (counseling)Medical prescriptionPopulationPrimary careNursingEnvironmental health

Abstract

fetched live from 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.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.367
Teacher spread0.325 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

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