From facts to feelings: Navigating the complexities of COVID-19 restrictions, perceptions, and mental well-being
Bibliographic record
Abstract
Objectives of the present study were to 1) examine accuracy of COVID-19 public health restriction knowledge and the impact of information source, 2) assess the effect of perceived level of restriction on perceived infection risk of COVID-19 infection and level of compliance with restrictions, and 3) investigate the relationship between mental health outcomes and perceived as well as actual level of restriction. Canadians (n = 5,051) completed an online survey between December 2020 and March 2021 assessing public health restriction knowledge, accuracy of this knowledge, information sources about COVID-19, perceived infection risk, compliance with restrictions, loneliness, anxiety, and depressive symptoms. Approximately half of our sample had accurate knowledge of the restrictions in their region/province, which significantly differed by province. Individuals who perceived restriction levels to be higher than they were, reported significantly greater perceived infection risk, more compliance with restrictions, worse mental health, and greater loneliness. Individuals living under moderate restrictions had better mental health and experienced less loneliness compared to minor, significant and extreme restriction levels. Findings suggest that while restrictions are beneficial for compliance, stronger and clearer restrictions should be coupled with mental health supports to remediate the negative effects of restrictions and uncertainty on mental health and loneliness.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".