Pandemic fatigue? Insights from geotagged tweets on the spatiotemporal evolution of mental health in Canadian cities during COVID-19
Bibliographic record
Abstract
While COVID-19 is no longer a global pandemic, its enduring effects on mental health persist. This is the first study to quantify the impact of the COVID-19 pandemic on population mental health in major Canadian cities. We track mental health dynamics of urban Canadian regions by monitoring the sentiment polarity dynamics, emotion trends, and top keywords of COVID-19 related discussions on Twitter (now X) from 2020 to 2022. Using over 430,000 geo-tagged posts, we combined geospatial mapping, machine learning, and social sensing to assess spatiotemporal variation in mental wellbeing across cities, interpreting underlying key factors and events that drove the mental “re-start” of a post-pandemic society. We found that early spring 2020 to summer 2021 was associated with increasing optimism, which progressed to a decline that persisted until the end of 2022. We observed spatial inequalities in population mental health across and within Vancouver, Calgary, Edmonton, Toronto, and Ottawa-Gatineau, which are predominantly English-speaking regions, and Montréal, a bilingual French-English region. In comparison to other English-speaking cities in the east coast, Toronto's maximum sentiment score was the lowest. Edmonton's maximum sentiment score was the lowest among all cities. Our results suggest that boosting public confidence and rebuilding psychological resilience are important in a post-pandemic era, and that interventions should be considered to address pandemic fatigue.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".