Initial risk factors, self-compassion trajectories, and well-being outcomes during the COVID-19 pandemic: A person-centered approach
Bibliographic record
Abstract
Introduction: We investigated whether initial risk classes and heterogeneous trajectories of self-compassion over the course of the pandemic may impact well-being outcomes 1 year into the pandemic. Methods: = 3,613; 50.6% women) was sampled longitudinally over 11 waves (April 2020-April 2021), using a rolling cross-sectional survey design. Analyses were conducted in three steps: (1) latent class analysis to identify heterogeneity in risk factors (sociodemographic, cognitive-personality, health-related) early in the pandemic, (2) latent class growth analysis (LCGA) to identify longitudinal self-compassion trajectories, and (3) GLM to examine effects of risk factor classes and self-compassion trajectories, as well as their interaction, on later well-being (mental health, perceived control, life satisfaction). Results and Discussion: Four risk factor classes emerged, with 50.9% of participants experiencing low risk, 14.3% experiencing multiple risks, 20.8% experiencing Cognitive-Personality and Health risks, and 14.0% experiencing sociodemographic and Cognitive-Personality risks. Four self-compassion trajectories also emerged, with 47.7% of participants experiencing moderate-high self-compassion that decreased then stabilized, 32.0% experiencing moderate self-compassion that decreased then stabilized, 17.3% experiencing high and stable self-compassion across time, and 3.0% experiencing low and decreasing self-compassion. Comparisons of well-being outcomes 1 year post-pandemic indicated that higher levels of self-compassion over time may protect against the impact of initial risk on well-being outcomes. Further work is still needed on heterogeneity in experiences of risk and protective factors during stressful life events.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".