Age-specific determinants of psychiatric outcomes after the first COVID-19 wave: baseline findings from a Canadian online cohort study
Bibliographic record
Abstract
BACKGROUND: Canadians endured unprecedented mental health (MH) and support access challenges during the first COVID-19 wave. Identifying groups of individuals who remain at risk beyond the acute pandemic phase is key to guiding systemic intervention efforts and policy. We hypothesized that determinants of three complementary, clinically actionable psychiatric outcomes would differ across Canadian age groups. METHODS: The Personal Impacts of COVID-19 Survey (PICS) was iteratively developed with stakeholder feedback, incorporating validated, age-appropriate measures. Baseline, cross-sectional online data collected between November 2020-July 2021 was used in analyses. Age group-specific determinants were sought for three key baseline MH outcomes: (1) current probable depression, generalized anxiety disorder, obsessive-compulsive disorder and/or suicide attempt during COVID-19, (2) increased severity of any lifetime psychiatric diagnosis, and (3) inadequate MH support access during COVID-19. Multivariable logistic regression models were constructed for children, youth (self- and parent-report), young adults (19-29 years) and adults over 29 years, using survey type as a covariate. Statistical significance was defined by 95% confidence interval excluding an odds ratio of one. RESULTS: Data from 3140 baseline surveys were analyzed. Late adolescence and early adulthood were identified as life phases with the worst MH outcomes. Poverty, limited education, home maker/caregiver roles, female and non-binary gender, LGBTQ2S + status and special educational, psychiatric and medical conditions were differentially identified as determinants across age groups. INTERPRETATION: Negative psychiatric impacts of COVID-19 on Canadians that include poor access to MH support clearly persisted beyond the first wave, widening pre-existing inequity gaps. This should guide policy makers and clinicians in current and future prioritization efforts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".