Creativity, Boredom Proneness and Well-Being in the Pandemic
Bibliographic record
Abstract
Throughout the course of the pandemic, it has become clear that the strictures of social isolation and various levels of lockdown constraints have impacted people's well-being. Here, our aim was to explore relations between trait dispositions associated with boredom proneness, self-regulation and well-being using data collected early in the pandemic. Specifically, we explored whether the tendency to engage in everyday creative pursuits (e.g., making your own greeting cards) would act as a prophylactic against poor well-being. Results showed that well-being was higher for those individuals who increased engagement with creative pursuits during the early stages of the pandemic. That is, people who engaged more in everyday creative activities also reported higher levels of self-esteem, optimism, and positive affect. In contrast, those who pursued fewer creative outlets had higher levels of depression and anxiety, were higher in boredom proneness, and reported experiencing more negative affect. As we emerge from the pandemic, these data provide a clue as to how people might plan to cope adaptively with the restrictive circumstances this extreme world event engendered. More generally, these data provide support for the notion that everyday creativity (and not necessarily creative expertise) has positive associations for well-being.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".