A longitudinal approach to understanding boredom during pandemics: The predictive roles of trauma and emotion dysregulation
Bibliographic record
Abstract
Research during the COVID-19 pandemic and prior outbreaks suggest that boredom is linked to poor compliance with critical lifesaving social distancing and quarantine guidelines, as well as to numerous mental health difficulties. As such, continued understanding on what contributes to boredom is imperative. Extending beyond the roles of constraint, monotony, and trait dispositions (e.g., individual differences in boredom propensity), and informed by prior theories on the emotional contributors of boredom, the current longitudinal study examined the predictive role of “pandemic trauma” on people’s boredom, with a focus on how emotion dysregulation mediates this relationship. Community participants (N = 345) completed questionnaires three times across an average of 3 1/2 weeks, rating their pandemic trauma, emotion dysregulation, and boredom over the past week each time. Pandemic trauma was assessed with items querying exposure to coronavirus, as well as the financial, resource-related, and interpersonal pandemic stressors that participants experienced. Emotion dysregulation was assessed with the Difficulties in Emotion Regulation Scale. Boredom was assessed with the short-form Multidimensional State Boredom Scale. The results of a theory-informed mediation model showed that participants’ pandemic trauma at Time 1 positively and modestly predicted their boredom at Time 3 and that this relationship was partially and moderately mediated by participants’ lack of emotional clarity and difficulties with engaging in goal-directed behaviors at Time 2. When people experience pandemic-related trauma, they subsequently struggle to understand their feelings and engage in goal-oriented actions, and, in turn, feel more bored. Theoretical and clinical implications as related to the emotional underpinnings of boredom are discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".