Familial Mechanisms Linking COVID-19 Lockdown with Mental Health Problems in Singaporean Children and Adolescents
Bibliographic record
Abstract
The COVID-19 pandemic and related confinement measures have profound impacts on the mental health of children and adolescents. This study investigated familial mechanisms underlying such impacts by studying the unique case of Singapore, which has undergone a period of local transmission without many restrictions (February–March 2020), followed by a nationwide lockdown (April–May 2020). In June 2020, we collected retrospective reports on everyday activities, social relationships, and mental health of 164 children and adolescents (Mage = 14.3, Range = 7-18, 49% female) across three timepoints: Pre-pandemic, Pre-lockdown, and Lockdown. Parental mental health, parenting values and practices were also collected. Multilevel modelling showed increased mental health problems in children from Pre-pandemic to Pre-lockdown, and from Pre-lockdown to Lockdown. Mediation analyses indicated that the increase in children’s mental health problems from Pre-pandemic to Pre-lockdown was partially explained by a decrease in time spent outdoors, an increase in their conflict with fathers, and an increase in their mothers’ mental health problems, with the last factor being the only significant mediator when all three factors were considered simultaneously. The increase in children’s mental health problems from Pre-lockdown to Lockdown was partially explained by a further increase in their mother’s mental health problems. These findings suggest that lockdown measures issued by authority may lead to further deterioration of children’s mental health beyond the pandemic itself. Multiple personal and familial factors may underlie these deteriorations, among which maternal mental health could be especially influential and should be targeted in family support service and intervention.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".