Personal Growth and Well-Being in the Time of COVID: An Exploratory Mixed-Methods Analysis
Bibliographic record
Abstract
The physical distancing measures necessitated by COVID-19 have resulted in a severe withdrawal from the patterns of daily life, necessitating significantly reduced contact with other people. To many, such withdrawal can be a major cause of distress. But, to some, this sort of withdrawal is an integral part of growth, a pathway to a more enriching life. The present study uses a sequential explanatory QUAN-qual design to investigate whether people who felt that their lives had changed for the better after being forced to engage in physical distancing, what factors predicted such well-being, and how they spent their time to generate this sense of well-being. We invited 614 participants who reported closely following physical distancing recommendations to complete a survey exploring this topic. Our analyses, after controlling for all other variables in the regression model, found a greater positive association between presence of meaning in life, coping style, and self-transcendent wisdom and residualized current well-being accounting for retrospective assessments of well-being prior to physical distancing. An extreme-case content analysis of participants' personal projects found that participants with low self-transcendent wisdom reported more survival-oriented projects (e.g., acquiring groceries or engaging in distracting entertainments), while participants reporting high self-transcendent wisdom reported more projects involving deepening interactions with other people, especially family. Our findings suggest a more nuanced pathway from adversity to a deeper sense of well-being by showing the importance of not merely coping with adversity, but truly transcending it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".