Predictors of time-varying and time-invariant components of psychological distress during COVID-19 in the U.K. Household Longitudinal Study (understanding society).
Bibliographic record
Abstract
= 17,761, April 2020-March 2021). Using the General Health Questionnaire-12 (GHQ-12), analyses encompassed (a) five annual waves before COVID-19 plus the first survey wave during COVID-19 and (b) eight (bi)monthly waves during COVID-19. We investigated (a) longitudinal measurement invariance of distress, (b) time-invariant and time-varying variance components of distress using latent trait-occasion modeling, and (c) predictors of these different variance components. In all analyses, unique measurement invariance in distress was established, indicating the same unidimensional construct was measured using the GHQ before and during COVID-19. Time-varying variance was higher at the first COVID-19 lockdown (April 2020, 61.2%) compared to before COVID-19 (∼50%), suggesting increased fluctuations in distress at the start of the pandemic. Sensitivity analyses with equal time lags pre- and during COVID-19 confirmed this interpretation. During the pandemic, the highest distress time-varying variance (40.7%) was detected in April 2020, decreasing to 29.0% (July 2020) after restrictions eased. Despite mean-level fluctuations, time-varying variance remained stable during subsequent lockdowns, indicating more rank-order stability after this first major disruption. Loneliness most strongly predicted time-varying variance during the first lockdown. Life dissatisfaction and financial difficulties were associated with both variance components throughout the pandemic. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".