Factors associated with change in moderate or severe symptoms of anxiety and depression in community-living adults and older adults during the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVES: Few are the longitudinal studies on the changes in moderate or severe symptoms of anxiety or depression (MSS-ANXDEP) from before to during the COVID-19 pandemic in Canada. The aim was to study the change in MSS-ANXDEP and associated sociodemographic, economic, psychosocial, health behaviour and lifestyle, and clinical factors. METHODS: The current sample includes 59,997 adults aged ≥ 35 years participating in the 2018 and 2020 health surveys of the 5 established cohorts of the Canadian Partnership for Tomorrow's Health (CanPath). MSS-ANXDEP was based on a cutoff score ≥ 10 on the 7-item Generalized Anxiety Disorder Scale and Patient Health Questionnaire (PHQ-8). Change in MSS-ANXDEP was categorized as follows: no MSS-ANXDEP, remitted, incident, and persistent. Multinomial regressions were used to study MSS-ANXDEP as a function of sociodemographic, economic, psychosocial, health behaviours and lifestyle, and clinical factors. RESULTS: Sociodemographic and economic (i.e. age, gender, cohort, race/ethnicity, lower income, decreased in income, work status, being an essential worker), lifestyle and health behaviours (i.e. smoking, cannabis and alcohol use, drinking more alcohol), psychosocial (i.e. provide help to others, information and instrumental support, and change in relationships with friends, family, and partner) and clinical factors (i.e. lifetime mental disorder and multimorbidity) were associated with remitted, incident, and persistent MSS-ANXDEP. CONCLUSION: Health and socio-economic factors were associated with changes in symptoms of anxiety and depression during the pandemic, further increasing inequities in mental health needs. Public health campaigns on the importance of healthy behaviours should continue and health policies should reduce economic and social barriers to integrated substance use and mental health care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".