Sources of COVID-19 Information Seeking and their Associations with Self-Perceived Mental Health among Canadians
Bibliographic record
Abstract
Using two datasets from the Canadian Perspectives Survey Series (CPSS), this study provides a longitudinal analysis of information sources Canadians consulted regarding COVID-19, and their associations with poor self-perceived mental health (SPMH) during March and July 2020. Nearly 20% of Canadians reported poor SPMH. The logistic regression results revealed that at Time 2 (July 2020), after controlling for demographic, socio-economic, and psycho-behavioural factors, using social media was significantly associated with higher odds of poor SPMH than using six other information sources including news outlets, federal health agencies, provincial health agencies, provincial daily announcements, places of employment, and other sources (for example, schools, colleges, universities). Checking the accuracy of online information more frequently was also associated with lower odds of poor SPMH.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".