Emotional response patterns, mental health, and structural vulnerability during the COVID-19 pandemic in Canada: a latent class analysis
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has contributed to increases in negative emotions such as fear, worry, and loneliness, as well as changes in positive emotions, including calmness and hopefulness. Alongside these complex emotional changes has been an inequitable worsening of population mental health, with many people experiencing suicidal ideation and using substances to cope. This study examines how patterns of co-occurring positive and negative emotions relate to structural vulnerability and mental health amid the pandemic. METHODS: Data are drawn from a cross-sectional monitoring survey (January 22-28, 2021) on the mental health of adults in Canada during the pandemic. Latent class analysis was used to group participants (N = 3009) by emotional response pattern types. Descriptive statistics, bivariate cross-tabulations, and multivariable logistic regression were used to characterize each class while quantifying associations with suicidal ideation and increased use of substances to cope. RESULTS: A four-class model was identified as the best fit in this latent class analysis. This included the most at-risk Class 1 (15.6%; high negative emotions, low positive emotions), the mixed-risk Class 2 (7.1%; high negative emotions, high positive emotions), the norm/reference Class 3 (50.5%; moderate negative emotions, low positive emotions), and the most protected Class 4 (26.8% low negative emotions, high positive emotions). The most at-risk class disproportionately included people who were younger, with lower incomes, and with pre-existing mental health conditions. They were most likely to report not coping well (48.5%), deteriorated mental health (84.2%), suicidal ideation (21.5%), and increased use of substances to cope (27.2%). Compared to the norm/reference class, being in the most at-risk class was associated with suicidal ideation (OR = 2.84; 95% CI = 2.12, 3.80) and increased use of substances to cope (OR = 4.64; 95% CI = 3.19, 6.75). CONCLUSIONS: This study identified that adults experiencing structural vulnerabilities were disproportionately represented in a latent class characterized by high negative emotions and low positive emotions amid the COVID-19 pandemic in Canada. Membership in this class was associated with higher risk for adverse mental health outcomes, including suicidal ideation and increased use of substances to cope. Tailored population-level responses are needed to promote positive coping and redress mental health inequities throughout the pandemic and beyond.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".