Trends and Correlates of Cigarette Smoking and Its Impacts on Health-Related Quality of Life Among People Living with HIV: Findings from the Ontario HIV Treatment Network Cohort Study, 2008–2014
Bibliographic record
Abstract
We sought to examine the trends of cigarette smoking, identify correlates of smoking, and examine the impacts of smoking on health-related quality of life (HRQOL) among people living with HIV in Ontario, Canada. Study sample included 4473 individuals receiving care and enrolled in the Ontario HIV Treatment Network Cohort Study. Self-report data on cigarette smoking, HRQOL, and demographic and sociobehavioral variables were collected between 2008 and 2014 through annual face-to-face interviews. Clinical data were abstracted from participants' medical records and enhanced through linkage with a provincial public health laboratory database. Analyses included descriptive statistics, generalized logit regression, and linear mixed-effects modeling. At first interview, 1760 participants (39.3%) were current cigarette smokers. Smoking prevalence declined annually by 1.6% between 2008 and 2014, but remained much higher than the prevalence in the general population. Current cigarette smokers were more likely to be younger, male, white or indigenous, Canadian-born, single, unemployed with lower education, heavy drinkers, nonmedicinal drug users, and to have current depression than former cigarette smokers or those who never smoked. Current cigarette smokers also had significantly (p < 0.001) worse SF-12 physical component summary (β = -2.07) and SF-12 mental component summary (β = -1.08) scores than those who never smoked after adjusting for demographic, socioeconomic, and HIV-related clinical variables. To reduce the burden of cigarette smoking, cessation interventions that take into account the complex social, economic, and medical needs of people living with HIV are needed urgently.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".