Lifestyle Related Risk Factors for Children’s Mental Health Problems: Exploring Sibling Influences in a Clinical Sample
Bibliographic record
Abstract
Research has clearly linked life disruptions brought about by the COVID-19 pandemic to variation in mental health problems in children. Indeed, lifestyle behaviours that were altered during the pandemic, such as sleep patterns, screen time, and physical activity, are established predictors of mental health. That said, while many children navigate day-to-day activities and routines with siblings, there has been relatively less research examining how lifestyles and, relatedly, mental health are influenced by siblings’ activities and the quality of sibling relationships. To address this limitation, the present study examines predictors of mental health problems in a clinical sample, paying close attention to target-child and sibling physical activity, sleep, and media use, in addition to quality of the sibling relationship and household pandemic related disruption. Data come from a clinical sample of N=143 parents/caregivers with at least two children (age 8-18 years) referred to an outpatient mental health clinic between September 2020 to November 2021 in Toronto, Ontario. Prior to interventions taking place, caregivers completed self-report instruments in the following areas: COVID-19 family stressors, children’s sleep disturbance, physical activity, screentime/use of technology, mental health problems in the domains of anger, anxiety, depression and disruptive behaviour. A hierarchical regression will be utilized to examine the association linking sibling lifestyle behaviours/sibling relationship quality with target-child mental health, over-and-above the child’s own lifestyle behaviours, COVID-19 related disruption, and covariates (e.g., age, gender, race). The results of this study have implications to inform lifestyle recommendations to mitigate the risks of adverse mental health problems in the wake of COVID-19.
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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".