Treatment use patterns in a large extended-treatment tobacco cessation program: predictors and cost implications
Bibliographic record
Abstract
BACKGROUND: Tobacco dependence follows a chronic and relapsing course, but most treatment programmes are short. Extended care has been shown to improve outcomes. Examining use patterns for longer term programmes can quantify resource requirements and identify opportunities for improving retention. METHODS: We analyse 38 094 primary care treatment episodes from a multisite smoking cessation programme in Ontario, Canada that provides free nicotine replacement therapy (NRT) and counselling. We calculate distributional measures of weeks of NRT used, clinical visits attended and total length of care. We then divide treatment courses into four exclusive categories and fit a multinomial logistic regression model to measure associations with participant characteristics, using multiple imputation to address missing data. RESULTS: Time in treatment (median=50 days), visits (median=3) and weeks NRT used (median=8) were well below the maximum available. Of all programme enrolments, 28.8% (95% CI=28.3% to 29.3%) were single contacts, 31.3% (30.8% to 31.8%) lasted <12 weeks, 19.2% (18.8% to 19.6%) were ≥12 weeks with an 8-week interruption and 20.7% (20.3%-21.1%) were ≥12 weeks without interruptions. Care use was most strongly associated with participant age and whether the nicotine patch was dispensed at the first visit. CONCLUSION: Treatment use results imply that the marginal costs of extending treatment programmes are relatively low. The prevalence of single contacts supports additional engagement efforts at the initial visit, while interruptions in care highlight the ability of longer term care to address relapse. Results show that use of the nicotine patch is associated with retention in care, and that improving engagement of younger patients should be a priority.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".