Trajectories of change in internalizing symptoms during the COVID-19 pandemic: A longitudinal population-based study
Bibliographic record
Abstract
BACKGROUND: Longitudinal data indicates that the mental health of the general population may not have been as badly affected by the COVID-19 pandemic as some had feared. Most studies examining change in mental health during the pandemic have assumed population homogeneity which may conceal evidence of worsening mental health for some. In this study, we applied a heterogeneous perspective to determine if there were distinct groups in the population characterised by different patterns of change in internalizing symptoms during the pandemic. METHODS: Self-report data were collected from a nationally representative sample of Irish adults (N = 1041) at four time-points between April and December 2020. RESULTS: In the entire sample, mean levels of internalizing symptoms significantly declined from March to December 2020. However, we identified four distinct groups with different patterns of change. The most common response was 'Resilience' (66.7%), followed by 'Improving' (17.9%), 'Worsening' (11.3%), and 'Sustained' (4.1%). Belonging to the 'Worsening' class was associated with younger age, city dwelling, current and past treatment for a mental health problem, higher levels of empathy, and higher levels of loneliness. LIMITATIONS: Sample attrition was relatively high and although this was managed using robust statistical methods, bias associated with non-responses cannot be entirely ruled out. CONCLUSION: The majority of adults experienced no change, or an improvement in internalizing symptoms during the pandemic, and a relatively small proportion of adults experienced a worsening of internalizing symptoms. Limited public mental health resources should be targeted toward helping these at-risk individuals.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".