Resilience throughout and beyond COVID-19: a longitudinal analysis
Bibliographic record
Abstract
Defined as the ability to adapt to adversity with a positive and stable mindset, resilience should be an important factor in coping with long-term evolving setbacks such as the COVID-19 pandemic. Although the negative mental health impacts of the pandemic are well-documented, the course of resilience during the pandemic and recovery periods remains understudied. This study examined resilience trajectories among respondents in the Canadian Personal Impacts of COVID-19 Survey (PICS) who provided data for at least two timepoints (n = 741). Resilience was measured using the Connor-Davidson Resilience Scale (CD-RISC), and linear mixed models assessed for variations in resilience over time. Sociodemographic factors were introduced as fixed-effects variables to ascertain impacts on baseline resilience scores and temporal trends. Overall, resilience levels were low throughout the course of the study. The study sample’s median baseline resilience score was 26 (IQR 21–30), which is significantly lower than the 25th percentile CD-RISC score noted in a pre-pandemic American community survey. This remained relatively unchanged until month 20 of follow-up, when point resilience scores showed a subtle (under one point), yet significant uptick from baseline. Sociodemographic analysis showed that low income was consistently associated with lower resilience (1.8-point difference, SE = 0.5, p = 0.002) throughout the observational period. Participants with a psychiatric disorder history had lower baseline resilience compared to those without any psychiatric history (3.4-point difference, SE = .05, p < 0.001). This gap decreased to 2.0 points (SE = 0.6, p < 0.001) by 24 months post baseline, suggesting that this negative effect on resilience diminished over time.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".