Community stress and resilience during covid-19: Assessing the emotional profile of the City of Hamilton using a social media analysis
Bibliographic record
Abstract
This study investigated stress and resilience at the neighbourhood level in Hamilton Ontario in pre- and peri-pandemic conditions using a social media analysis. Sentiment analysis of geo-located Twitter posts produced within Hamilton census tract boundaries was conducted using Stresscapes and EMOTIVE, validated software that extract and code emotional information from human language expressions about stress and hope (a proxy for stress), respectively. Baseline levels of both emotions were measured using aggregate scores at the census tract level in Hamilton from tweets produced during two pre-pandemic periods (March 2019 to July 2019; and August 2019 to February 2020), with a replication analysis corresponding to the first (March - July 2020), second (August 2020 - February 2021) and third (March 2021-July 2021) waves of the pandemic. The spatial distribution of stress and hope across the five time periods (pre- and peri-pandemic) was visualized using a geographic information system. Candidate explanatory variables (including COVID-19 cases count, visible minority status, educational attainment, household income, and household size) were examined for significant bivariate correlations with the change in stress emotions within neighbourhoods across pre- and peri-pandemic periods. Baseline hope was examined as an effect modifier of any significant relationships between explanatory variables and stress. Results suggest that variation between stress and hope emotions exist between Hamilton census tracts (n=30) over the five time periods. Among the explanatory variables, household size and household income displayed a strong bivariate correlation to stress; however, baseline hope did not modify the effect on stress of either variable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".