Suicidal ideation among Canadian adults during the COVID-19 pandemic: the role of psychosocial factors and substance use behaviours
Bibliographic record
Abstract
BACKGROUND: Suicide is one of the most important and increasing public health agenda around the world. Since the COVID-19 pandemic, concerns have been raised about the potential adverse impacts of the pandemic on suicide-related outcomes. The main objective of this study was to examine the association of psychosocial risk factors (mental health illnesses and social isolation) and substance use behaviors (cannabis and alcohol consumption) with suicidal ideation during the COVID-19 pandemic among Canadian adults. METHODS: The study was conducted based on a total of 4005 persons 18 years of age or older, living in Canada's ten provinces. The data used in this study were collected during April 20-28, 2021, by Mental Health Research Canada. Multivariable logistic regression was used to determine the association of mental health conditions (anxiety, depression, and other mood disorder) before and since COVID-19 outbreaks, social isolation and living arrangement, as well as cannabis and alcohol consumption with suicidal ideation during COVID-19. RESULTS: The results of adjusted logistic regression showed that the odds of suicidal ideation were 1.526 times higher (95% CI:1.082-2.152) among those who reported continued negative impacts of social isolation. The odds of suicidal ideation were also higher for those who were diagnosed as having depression before (OR = 3.136, 95% CI: 2.376-4.138) and since the COVID-19 pandemic (OR = 3.019, 95% CI:1.929-4.726) and 1.627 times higher (95% CI: 1.225-2.163) for those who were diagnosed as having anxiety before the COVID-19 pandemic. Those who reported having increased and those who were consuming cannabis during the pandemic were 1.970 (95% CI: 1.463-2.653) and 1.509 times (95% CI: 1.158-1.966) more likely to have thought of suicide than non-takers, respectively. CONCLUSION: Given the significant associations of psychosocial factors (mental health illnesses and social isolation) and cannabis use with suicidal ideation, more attention and support need to be given to adults who had mental health conditions before and since COVID-19, those who were negatively impacted by social isolation, and those are exposed to substance use (cannabis).
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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 | 0.003 | 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".