Extraversion in COVID-19 Coping and Actionable Insights from Considering Self-Directed Learning
Bibliographic record
Abstract
Extraversion, of the Big Five personality traits, has been identified as the most socially relevant of the traits with respect to positive COVID-19 coping—yet relevant research is found conflicting. Studies assessing this discrepancy have not situated the influence of extraversion within a geographical and historical context. Thus, a likely contributor has been missed. Furthermore, extraversion is based on other-directed learning with respect to COVID-19 coping, and this has not been considered regarding its contrast to self-directed learning. To provide context, an examination of high-ranking Google Scholar results on extraversion and COVID-19 coping from different countries during the pandemic’s various waves is undertaken, including the introduction of vaccines as a factor in decreasing COVID-19’s perceived threat. These are then examined for relationships regarding public opinion. Following, extraversion is compared with other-directed learning and differentiated from self-directed learning. An understanding is thus presented for assessing when extraversion will be an effective personality trait for positive COVID-19 coping and when it will not. Extraversion’s effect is found inherently inconsistent for identifying positive COVID-19 coping because of its dependence on other-directed learning. The conclusion: stability in positive COVID-19 coping is contingent on personal values that guide self-directed learning rather than extraversion’s other-directed learning.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".