Exploring Living Arrangements as a Predictor of Canadians’ Illicit Drug Use: Quantitative Findings From the Canadian Community Health Survey
Bibliographic record
Abstract
Objectives: About four percent of Canadians used illegal drugs in 2019, but it remains unknown whether their living arrangements are a relevant factor. Methods: We use the public version of the 2015–2016 Canadian Community Health Survey Annual Component. The binary logit model and complementary log-log model are applied to investigate to what extent living arrangements predict Canadians’ recent illicit drug use. Results: Living alone is significantly associated with Canadians’ illicit drug use. For young and older Canadians, those living with spouses/partners, children, or both are less likely to use illicit drugs than their solo-living counterparts. Middle-aged Canadians who lived with spouses/partners only or with children have significantly lower likelihoods of using illicit drugs compared to those living alone. Additionally, differences between men and women have been found. Spouses/partners and children play more positive roles for young and middle-aged women than for men. Conclusion: Our findings suggest that living with core families is a type of collectivity that may have positive effects on Canadians’ health behaviours compared to those living alone, who, therefore, need more attention from health officials.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".