Identifying Barriers to Entering Smoking Cessation Treatment Among Socioeconomically Disadvantaged Smokers
Bibliographic record
Abstract
Abstract Background:Efficacious smoking cessation interventions exist, yet few smokers utilise available resources such as psychosocial treatment programs and pharmacotherapy. The goals of the present study were to (1) identify perceived barriers to entering smoking cessation treatment programs among socioeconomically disadvantaged smokers, who are presently underrepresented in smoking cessation interventions; (2) determine what variables are most important in predicting the barriers identified (i.e., age, gender, ethnicity, income, nicotine dependence level, smoking rate, years smoking, stage of change, presence of smoking-related illness and medical insurance status).Methods:Responses from socioeconomically disadvantaged smokers (N= 343) were collected in 2004–2005 and analysed to develop the Treatment Barriers Questionnaire, a 40-item measure of reasons for not entering smoking cessation programs. Study methods were approved by the Institutional Review Board of Louisiana State University; informed consent procedures were employed.Results:Principal components analysis yielded seven scales named for their theme: (1) Preparedness to Quit Smoking; (2) Work and Time Constraints; (3) Smokers Can or Should Quit on Own; (4) Opinions about Professional Assistance; (5) Mobility Limitations; (6) Insurance Limitations and (7) Misinformation about Professional Assistance. Gender, ethnicity, daily smoking rate, nicotine dependence and stage of change were significant predictors in regression analyses for scales 1,F(10, 201) = 7.83,p< .001,R2= .29, 2F(10, 201) = 2.30,p< .05,R2= .11, and 3,F(10, 201) = 3.58,p< .001,R2= .16. Conclusions: Results can inform efforts to facilitate entry and retention of smokers in cessation programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".