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Record W3215343182 · doi:10.1093/jnci/djab209

Overcoming “Cessation Stasis”: The Need to Address Inertia

2021· letter· en· W3215343182 on OpenAlexaff
Andrew Pipe

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2021
Typeletter
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsInertiaMedicineSmoking cessationPhysical medicine and rehabilitationPathologyPhysics

Abstract

fetched live from OpenAlex

No one can doubt the importance of smoking cessation as a fundamental preventive initiative (1,2). The dramatic reductions in morbidity and mortality that follow cessation argue that cessation interventions should be an integral part of clinical care. But despite a substantial reduction in smoking rates in recent decades, there is now disconcerting, if not dispiriting, news regarding a decline in cessation rates in the United States. In this issue of the Journal, Leventhal and colleagues (3) report that declines in smoking have slowed following decades of reductions, particularly among minority, disadvantaged, and rural groups. Their conclusions are based on a careful analysis of successive iterations of the Tobacco Use Supplement of the Current Population Survey, affording a robust examination of population smoking behaviors and a nuanced understanding of cessation activities among groups typically not captured in standard cessation research. Their findings reveal that there was virtually no change in cessation activities from 2014 to 2019 and that sociodemographic disparities in cessation behaviors were prominent. Overall interest in cessation remained constant: 77.1% of smokers voiced a desire to quit, but sustained cessation rates (7.5%) were unchanged (3). More disquieting, rates of smoking cessation have declined among the most vulnerable segments of the population, where rates of smoking and smoking-related disease are high. Equally concerning, only a minority of smokers (34.4%) employ smoking cessation treatments when attempting to quit, and predictable discrepancies in the use of cessation supports exist among racialized and disadvantaged communities. Many of the conventional approaches intended to support cessation—quit lines, subsidized nicotine-replacement therapy, and digital treatment applications—attracted only modest use. The findings identify real challenges for clinicians and public-health organizations. How can we address our failure to provide supported cessation opportunities to those who have interest in cessation but receive little assistance? In clinical settings, inadequate approaches to addressing tobacco addiction are, sadly, commonplace. The introduction of integrated cessation programs in hospital settings has been recommended for decades (4-6). Evidence of their clinical significance and their ability to reduce cost and subsequent use of health-care resources continues to accumulate (7-10). Smoking cessation at the time of cancer diagnosis can enhance quality of life, reduce treatment complications, and prolong survival (11). Recommendations for the incorporation of smoking-cessation services as a standard of cancer care abound and should be heeded (12-14). Barriers remain but can be overcome; the time to ensure the systematic integration of smoking-cessation services in clinical settings is long overdue (15-17). Notwithstanding our ability to increase the likelihood of smoking cessation success, the challenge of smoking cessation is substantial. How can we combat the activities of an industry unrivalled in its ability to evade regulation and to cause enormous human loss? Although efforts to assist smokers should be expanded and systematized, it is equally important that as clinicians we advocate for much more robust and far-reaching regulation of the tobacco industry—an industry unparalleled in its record of duplicity and destruction. None. Role of the funder: Not applicable. Disclosures: The author has no conflicts of interest to disclose. Author contributions: Writing, original draft, revisions—AP. No new data are presented in this editorial.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.050
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.047
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0100.010
Scholarly communication0.0080.011
Open science0.0020.007
Research integrity0.0500.066
Insufficient payload (model declined to judge)0.0090.003

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 head0.102
GPT teacher head0.367
Teacher spread0.265 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations2
Published2021
Admission routes1
Has abstractno

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