Temporal distancing during the COVID‐19 pandemic: Letter writing with future self can mitigate negative affect
Bibliographic record
Abstract
Novel coronavirus disease (COVID-19) is spreading across the world, threatening not only physical health but also psychological well-being. We reasoned that a broadened temporal perspective may attenuate current mental distress and tested a letter-writing manipulation designed to connect people to their post-COVID-19 future selves. We conducted an online experiment with 738 Japanese participants recruited from two common survey platforms. They were randomly assigned to either send a letter to their future self (letter-to-future) condition, send a letter to present self from the perspective of future self (letter-from-future) condition, or a control condition. Participants in both letter-writing conditions showed immediate decrease in negative affect and increase in positive affect relative to the control condition. These effects were mediated by temporal distancing from the current situation. These findings suggest that taking a broader temporal perspective can be achieved by letter writing with a future self and may offer an effective means of regulating negative affect in a stressful present time such as the COVID-19 pandemic.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".