Individualized Treatment for Tobacco Dependence in Addictions Treatment Settings: The Role of Current Depressive Symptoms on Outcomes at 3 and 6 Months
Bibliographic record
Abstract
INTRODUCTION: Individuals with concurrent tobacco dependence and other addictions often report symptoms of low mood and depression and as such may have more difficulty quitting smoking. We hypothesized that current symptoms of depression would be a significant predictor of quit success among a group of smokers receiving individualized treatment for tobacco dependence within addiction treatment settings. METHODS: Individuals in treatment for other addictions were enrolled in a smoking cessation program involving brief behavioral counseling and individualized dosing of nicotine replacement therapy. The baseline assessment included the Patient Health Questionnaire (PHQ9) for depression. Smoking cessation outcomes were measured at 3 and 6 months post-enrollment. Bivariate associations between cessation outcomes and PHQ9 score were analyzed. RESULTS: Of the 1,196 subjects enrolled to date, 1,171 (98%) completed the PHQ9. Moderate to severe depression (score >9) was reported by 28% of the sample, and another 29% reported mild depression (score between 5 and 9). Contrary to the extant literature and other findings by our own group, there was no association between current depression and cessation outcome at either 3 months (n = 1,171) (17.0% in those with PHQ9 > 9 vs. 19.8% in those with PHQ9 < 5, p = .32) or 6 months (n = 834) (17.8% vs. 18.9%, p = .74). CONCLUSIONS: Contrary to our hypothesis, depression severity as measured by the PHQ9 did not predict cessation outcome in this clinical population. A possible explanation may be the individualized treatment and supportive environment of an addictions treatment setting. These data indicate that patients in an addictions treatment setting can successfully quit smoking regardless of current depressive symptoms.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".