A Diary Study on When and With Whom Recovery Experiences Modulate Daily Stress and Worry During a COVID-19 Lockdown
Bibliographic record
Abstract
In April 2020, almost six out of 10 people around the world were in lockdown due to the COVID-19 pandemic. Being locked down usually has a deleterious effect on the confined individual's mental health. In this exceptionally challenging context, finding ways to minimize negative mood about the pandemic is essential. Pandemic-related negative states ("negative mood") and recovery experiences were investigated in a sample of 264 individuals who completed daily surveys four times per day over 7 consecutive days. MSEMs analyses revealed that negative mood persisted from moment-to-moment through the day, thus showing a response lag effect. Further analyses revealed that when someone experienced pandemic-related psychological detachment, relaxation, mastery, control, pleasure, or relatedness at specific periods of the day, mood had improved at the next measured time period, suggesting a protective effect. However, the pattern displayed by singles with dependents suggests that some recovery experiences at specific periods during the day seem to have a backfiring effect and worsen subsequent mood. These findings bring new insight into the role of recovery experiences during lockdowns and suggest that many could benefit from such experiences throughout the day when self-isolating. However, for individuals with multiple risk factors such as being single with dependents, some recovery experiences, at specific periods during the day, might not bring the desired outcome and future research is needed to examine if guilt or domestic burden may explain this finding. Results contribute to our understanding of how to take care of one's mental health during the current pandemic, and concrete recommendations adapted to individual contexts are provided.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".