The impact of personality traits on the positive mental health of Canadian adults during the COVID-19 pandemic: A Replication and Extension Study
Bibliographic record
Abstract
Abstract The COVID-19 epidemic was first reported in 2019 and rapidly spread across the globe. Many studies have shown that the COVID-19 pandemic adversely affected mental health. Individual differences such as personality could influence people’s responses to the pandemic. For example, the results of a previous study by Shokrkon and Nicoladis (2021) showed that the personality trait of Extroversion positively and Neuroticism negatively contributed to the mental health of Canadians. The goal of our study was to replicate this study using the same tasks and a similar population and extend it by including all 5 personality traits in our analysis and also controlling for the variable of Response to COVID-19 Stress (in addition to demographic variables). Our results were similar to Shokrkon and Nicoladis (2021) and we also found that Extroversion positively and Neuroticism negatively are associated to the mental health of Canadians. We also found that Agreeableness, Openness to Experience, and Conscientiousness are positively and significantly related to the mental health of Canadians. Our results could provide a guide for the screening of people more at risk for mental health issues based on personality traits.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".