The impact of job strain on smoking cessation and relapse in the Canadian population: a cohort study
Bibliographic record
Abstract
BACKGROUND: The aim of this study is to investigate the impact of job strain, as measured by the Karasek demand/control model (DCM), on smoking cessation and relapse in a representative general population sample. METHODS: A secondary analysis of data from the Canadian National Population Health Survey (NPHS) was undertaken. Daily smokers and former daily smokers (n=1287 and 1184, respectively) at cycle 1 (1994/1995) of the NPHS were followed up at cycle 2 (1996/1997). Measures of job strain (the independent variables) were based on data from cycle 1, predicting smoking status at cycle 2. Logistic regression analysis was employed in two ways. Individuals were stratified into job strain quartiles while continuous measures were also employed in separate analyses for job strain and its component dimensions. RESULTS: In the quartile analysis, no effect of job strain was observed on the likelihood of cessation, while a non-linear effect was observed on the likelihood of relapse, although this relationship lost significance (p>0.05 and <0.10) after controlling for personal characteristics. No effect was observed using the continuous measure of job strain or the continuous measure of job demand on either cessation or relapse. For job control, no effect was observed on the likelihood of cessation, but increased control was found to decrease the likelihood of relapse in the unadjusted model only. CONCLUSIONS: Psychosocial work environments may be too diverse for uniform trends in the relationship between job stress and smoking behaviour to emerge in a population sample. Future research should avoid use of the scaled-down DCM instrument where possible.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".