Relationship Between Personality Traits and Emotional Impacts of the COVID-19 Pandemic on Canadian Emerging Adults
Bibliographic record
Abstract
We assessed whether traits from the four-factor vulnerability model for substance misuse are associated with the content of emotional descriptions given by Canadian university students of the impact of the COVID-19 pandemic on their lives. Personality traits were measured in 1185 first- and second-year undergraduates (mean age = 19.11 years; 79% female). Written responses to "Tell us about how the COVID-19 pandemic is impacting your life" were coded using Linguistic Inquiry and Word Count software. Negative binomial analyses were run to examine links between traits and emotion word types used in responses. Anxiety sensitivity was associated with increased use of anxiety words; hopelessness was associated with increased use of negative emotion and sadness words, and decreased use of positive emotion words; and impulsivity was associated with increased use of anger words. Findings have implications for personality-tailored interventions for students vulnerable to distress resulting from highly stressful situations such as pandemics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".