Stuck in Time and Freed by Time: A Mixed-Methods Investigation of Subjective Time, Mental Time Travel, and Well-Being on the First and Second Anniversaries of the COVID 19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic posed unprecedented challenges to health and subjective well-being (SWB) and disruptions to routine that contributed, for many, to a distorted sense of time. In two cross-sectional surveys (N = 2133) collected on the first and second anniversary of the pandemic’s declaration (March 11, 2021 & 2022), we used quantitative and qualitative methods to document people’s subjective experience of time: its phenomenology (subjective distance and speed of the passage of time) and evaluation of time emptiness and urgency (feelings of too much and too little time). Our first Research Objective examined how people’s lifestyle behaviors predicted their experience of time and their SWB. Therapeutic lifestyle choices (well-being sustaining activities such as time in nature and hobbies) predicted less time emptiness (S1 & S2) and urgency (S2) and, in turn, greater SWB. Maintaining social relationships predicted SWB both in person and remotely; screentime (TV, video games) did not predict SWB. Our second Research Objective examined how mental time travel (MTT), an adaptive capability of human beings to imagine personal future and re-live past events, could contribute to well-being during a pandemic. MTT to both a past (pre-pandemic) and future (post-pandemic) desirable event improved positive mood relative to baseline, and future MTT both felt subjectively closer and improved mood more than past MTT. Results suggest the many ways people may find themselves stuck in time during such a time-distorting period, but also how their activities and mental time travel may help bolster SWB.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".