Pandemic fatigue? Insights from geotagged tweets on the spatiotemporal evolution of mental health in Canadian cities during COVID-19
Why this work is in the frame
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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. • Over 430,000 geo-tagged X posts were used to track mental health trends across six major cities using GIS and machine learning. • Our kernel density heatmap illustrated that people express more optimism in areas with more open blue and green space. • The lowest monthly sentiment score in our sentiment polarity detection aligned with the timing of the Freedom Convoy protests. • The keyword "vaccination" prompted strong sentiment from both proponents and opponents of vaccination during the second wave. • There is a strong link between financial support programs and mental wellness during the COVID-19 pandemic.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it