Relationship between substance use and perceived mental health using Stats Can 2021 Data
Bibliographic record
Abstract
During the COVID-19 pandemic, the use of substances and concerns about mental health have increased globally. For example, Czeisler and colleagues (2020) report that 13% of adults in the United States (during the year of 2020) increased their consumption of substances due to COVID-19 associated stress, and 11% of adults experienced thoughts of suicide in the past 30 days. A similar positive relationship between substance use and the pandemic was observed in France, Sweden, and England. Recent research purports that a change in people's lifestyle and living through moments of fear, and withdrawal from relationships and interactions as a result of social distancing are critical factors contributing to the negative health and social implications of COVID-19. The purpose of this research is to examine whether one's frequency and use of substances (e.g., alcohol, cannabis, and opioids) during the pandemic significantly predicts their subjective reporting of their overall mental health and their mental health now relative to pre-pandemic. Secondly, we sought to determine whether social connectedness and life satisfaction significantly moderates this relationship. We believe this is a critical relationship to investigate as unhealthy patterns developed during isolation periods of the pandemic and the resulting impact on mental health may have sustained effects in the long-run. It is plausible that individuals who have been able to maintain social connections throughout the pandemic were better able to cope with periods of isolation. This research will use the Canadian Perspective Survey Series 2021 from Statistics Canada, precisely the Substance Use and Stigma During the Pandemic dataset.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.005 | 0.001 |
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".