Clearing the Smoke Screen: Smoking, Alcohol Consumption, and Stress Management Techniques among Canadian Long-Term Care Workers
Bibliographic record
Abstract
BACKGROUND: Currently, there is abundant research indicating that smoking and alcohol consumption have significant impacts on morbidity and mortality, though little is known about these behaviors among Canadian health care workers. The objective of this study was to examine health and coping behaviors, such as smoking and alcohol consumption as well as stress management techniques, among health care workers consisting of gendered, racialized, and immigrant employees. METHODS: Drawing on a single-case, mixed-methods study in Ontario, Canada, this paper presents under-researched data about smoking practices, alcohol consumption, and stress management techniques among health care workers in labor-intensive, high-stress, high-turnover environments. In particular, it identifies the various mechanisms for maintaining health and well-being. RESULTS: The findings suggest that 7.7% of survey respondents reported smoking while 43.4% reported alcohol consumption, which were reported more frequently among immigrants than among non-immigrants. Participants also reported health-promoting activities in face-to-face interviews, such as mindful breathing techniques and drawing upon social support, while a few respondents reported alcohol consumption to specifically cope with sleep disturbances and job stress. CONCLUSIONS: Although smoking and alcohol consumption were both connected with coping strategies and leisure, they were predominant in immigrant groups compared to non-immigrant groups.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".