Trauma Symptomology During COVID-19 Among Youth with a Learning, Cognitive or Psychological Disorder Diagnosis: Exploring Moderation by Social Support
Bibliographic record
Abstract
During previous disasters, youth with existing mental health diagnoses have been especially vulnerable to negative mental health outcomes. Yet, longitudinal outcomes for these youth during the COVID-19 pandemic have not been well-studied. In addition, potential protective factors that may buffer this enhanced risk in the context of the pandemic also need to be explored. Thus, this longitudinal study investigates if having a self-reported learning, cognitive, and/or psychological disorder diagnosis placed adolescents at greater risk for trauma symptomology over the first full academic year (2020-21) of the COVID-19 pandemic, and if this relationship was moderated by peer or family support. To answer this question, we collected four waves of data from youth (M age = 14.63) in one province in Western Canada over the 2020/21 school year ( N = 1,227). Trauma symptomology was assessed using the Child Revised Impacts of Events Scale (CRIES-13). We used multivariate linear regression to assess if an existing learning, cognitive, and/or psychological disorder diagnosis was associated with trauma symptomatology in June 2021, controlling for symptoms in September 2021, and to explore potential moderation by peer and family support. We found that youth who had an existing learning, cognitive, and/or psychological disorder diagnosis reported significantly higher trauma symptomatology across the 2020/21 academic year, as compared to youth without a diagnosis, but that there was no moderation by peer or family support. Our results suggest that a small but significant subset of youth who were at risk for poor mental health outcomes prior to the pandemic remain vulnerable and require access to ongoing school psychology supports to promote their mental well-being.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".