Assessing the Pandemic's Impact on Mental Health Awareness: A Canadian Perspective Using Time Series Analysis
Bibliographic record
Abstract
The COVID-19 pandemic has had a significant impact on people’s mental health, especially increasing concerns about issues like depression and anxiety. This study analyzed weekly internet search data to observe how public interest in these mental health problems in Canada changed before and during the pandemic. Time series analysis methods were used, such as Seasonal-Trend Decomposition (STL), ARIMA modeling, and change point detection. The results showed that during the early stages of the pandemic, there was a large increase in searches for "depression" and "anxiety." The ARIMA model created a scenario where the pandemic did not happen, showing what the search patterns might have looked like without it. Change point detection also identified key moments, like the March 2020 lockdown, when search behavior changed significantly. Overall, the pandemic worsened public concerns about mental health, with noticeable differences compared to pre-pandemic trends. This shows that there is an urgent need for stronger mental health support during national crises. Future research could use clinical data to further validate these trends.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".