The impact of lockdown on child adjustment: a propensity score matched analysis
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has had an inestimable impact worldwide, challenging the daily lives and interactions of children and their families. In 2022, Shanghai implemented a three-month lockdown in response to an acceleration of positive cases during the pandemic period. This restrictive policy provided insight into the impact of the lockdown on children's social adjustment and the role of parent–child conflict during this process. Mothers of preschool-aged children participated in this study and completed the Chinese version of Child-Parent Relationship Scale (CPRS) and the Strengths and Difficulties Questionnaire (SDQ). Using Propensity Score Matching (PSM) method, two matched groups were formed: pre-lockdown group and post-lockdown group, with a total of 574 preschoolers (N = 297 in each group; Mage = 4.36, SD = 0.86) were recruited. The results showed that the lockdown directly impacted children's emotional symptoms. Additionally, the parent–child conflict mediated relationship between the lockdown and children's adjustment. Specifically, parent–child conflict deteriorated children's emotional symptoms, hyperactivity-attention problems, and prosocial behaviors. These findings highlight the significant impact of the severe lockdown on children's social adjustment and the role of parent–child interactions during this period.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".