MétaCan
Menu
← Back to cohort
Record W4319333008 · doi:10.2196/43689

Caregiver Perceptions of Children’s and Adolescents’ Psychosocial Functioning During the Stringent COVID-19 Lockdown Restrictions in Shanghai: Cross-sectional Study

2023· article· en· W4319333008 on OpenAlexvenueno aff
Xu Liu, Jing Wu, Hongyang Yang, Fangjie Zhao, Yuchen Qin, Jiali Wu, Hongli Yan, Yan Xu, Lulu Zhang

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsPsychosocialMental healthPublic healthPandemicCross-sectional studyChecklistMedicineLogistic regressionPsychologyCoronavirus disease 2019 (COVID-19)PsychiatryEnvironmental healthClinical psychologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 pandemic represents a global health crisis. The Shanghai municipal government in China implemented strict and comprehensive pandemic control strategies in the first half of 2022 to eliminate a wave of COVID-19 infection. The pandemic and the resulting government responses have led to abrupt changes to families' daily lives, including the mental health of children and adolescents. OBJECTIVE: The aim of this paper is to examine the impact of COVID-19 exposure and the stringent lockdown measures on the daily life and mental health of children and adolescents and to provide suggestions on maintaining their mental health when similar public health emergencies occur in the future. METHODS: In this cross-sectional study, an anonymous survey was distributed online in May 1-15, 2022, in Shanghai. Individuals were eligible to participate if they were currently the caregiver of a child or adolescent (aged 4-17 years). Outcomes were psychosocial functioning of children and adolescents, as reported by parents, using the Pediatric Symptom Checklist-17. COVID-19 exposure and life changes were also reported. Multivariate logistic regression was used to analyze risk factors for poor psychosocial functioning. RESULTS: In total, 2493 valid questionnaires were analyzed. The rate of positive scores on the global Pediatric Symptom Checklist-17 scale was 16.5% (n=411). Internalizing, attention, and externalizing problem subscale positivity rates were 17.3% (n=431), 10.9% (n=272), and 8.9% (n=221), respectively. Caregivers reported that 64.2% (n=1601) and 20.7% (n=516) of the children's interactions with friends or peers and parents deteriorated, respectively. Compared with male caregivers, female caregivers were less likely to report psychosocial problems in children and adolescents (adjusted odds ratio [aOR] 0.68; 95% CI 0.53-0.88). Older children and those with lower COVID-19 Exposure and Family Impact Scales scores were less likely to have psychological problems (aOR 1.15; 95% CI 1.10-1.21). Compared with children with screen times <1 hour per day for recreation, those using screens for >3 hours had higher odds of psychological distress (aOR 2.09; 95% CI 1.47-1.97). Children who spent 1-2 hours exercising and had better interactions with friends or peers and parents showed a trend toward lower odds of psychological problems. Children and adolescents with worse sleep compared with preclosure were more likely to have psychological problems. CONCLUSIONS: The prevalence of psychosocial problems among children and adolescents is relatively high. Being young, having more COVID-19 exposure, and having more screen times (>3 h/day), less exercise time (<30 min), worse sleep, and deteriorated interactions with friends or peers and parents were risk factors for poor psychosocial functioning. It is necessary for governments, communities, schools, and families to take appropriate countermeasures to reduce the negative impact of the stringent control measures on caregivers' parenting and psychosocial functioning of children and adolescents.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.061
GPT teacher head0.408
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Public Health and Surveillance→Same topicCOVID-19 and Mental Health→French-language works237,207→