Examining the Longitudinal Associations between Adjustment Disorder Symptoms and Boredom during COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has led to a myriad of stressors, underscoring the relevance of adjustment disorder during these extraordinary times. Boredom—as a feeling and as a dispositional characteristic—is an equally pertinent experience during the pandemic that has been cross-sectionally linked to various mental health difficulties. The current longitudinal study expanded on this work, examining the associations between adjustment disorder symptoms and boredom (both as a feeling and as a trait) over time during the COVID-19 pandemic. Community participants completed questionnaires three times, rating their trait boredom at Time 1 and their feelings of boredom and adjustment disorder symptoms (preoccupation with a pandemic stressor and failure to adapt) over the past week at Times 1–3. Latent growth curve analyses found that an increase in feelings of boredom was significantly associated with increased preoccupation with a pandemic stressor and increased difficulties with adapting over time. Additionally, trait boredom significantly predicted changes in preoccupation and the failure to adapt, such that participants high in trait boredom increasingly struggled with these symptoms over time. Our results suggest that increased feelings of boredom and a trait disposition towards boredom can be detrimental to people’s ability to adjust over time to the stressors associated with the pandemic. Boredom, as an aversive state and as a chronic difficulty, may be important to address in treatment approaches for adjustment disorder symptoms during COVID-19.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".