Changes to Family Life, Youth COVID-19 Pandemic-Related Traumatic Stress, and the Youth Mental Health Crisis
Bibliographic record
Abstract
Objective Traumatic stress symptoms increase the risk for mental health problems. We examine patterns of COVID-19-related changes in youth and family experiences (material hardships, behavior change, coping strategies), how these patterns vary with sociodemographic factors, and how COVID-19-related experiences associate with youth pandemic-related traumatic stress (PTS) symptoms.Method K-means clustering examined patterns of pandemic-related experiences in Environmental influences on Child Health Outcomes data (April 2020-August 2021; N = 9,139; 48% female), a demographically and regionally diverse sample. Clusters were characterized by sociodemographic factors measured pre-pandemic. Sparse partial least squares regression evaluated associations between cluster parameters and youth PTS symptoms in two samples (children [<13 years-old, n = 1,293]; adolescents [≥13 years-old], n = 1,272).Results Clustering replicated in the child and adolescent samples. One cluster reported more (HiChange) and one reported less (LoChange) pandemic-related change. The LoChange (versus HiChange) group included more Black individuals, single-parent households, and had lower income and education. PTS Scale scores were more associated with the youth’s own versus the parent/caregiver’s experiences. Nonetheless, across all youth, a report of “no change” in parent/caregiver behavior was associated with lower youth PTS Scale scores. For all children, lower PTS Scale scores are associated with the parent/caregiver being able to isolate. Use of coping strategies was not associated with lowered scores. Higher scores are associated with changes in youth health behaviors (e.g. eating, exercise, time outside), health care access, and increased media use.Conclusion Results provide information for public health guidance, which can minimize youth PTS symptoms now and in future health disasters: stability in health behaviors, access to healthcare, and ability to isolate are paramount.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".