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Record W7045717416

Association between family contextual factors and child mental health during the third wave of the pandemic in Ontario: A cross-sectional analysis

2022· dissertation· en· W7045717416 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2022
Typedissertation
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthAffect (linguistics)Association (psychology)StressorPandemicSample (material)Child healthOnly child
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: In Ontario, school closures due to the pandemic lasted 20 weeks at various times throughout March 2020 to June 2021; longer than any Canadian province or territory. School closures may have had a detrimental impact on school-aged children’s academic and psychological functioning. OBJECTIVES: The objective was to examine the variability in child mental/emotional mental health in association with family factors (caregiver depression, caregiver anxiety, overreactive parenting, partner conflict, work-family conflict) and COVID-19 experiences (health-related stressors, resource-related stressors, positive experiences) among children from two-caregiver working households in Ontario METHODS: Data came from the second iteration of the Ontario Parent Survey. The cross-sectional analysis (n=5787) was restricted to working adults, part of a two-caregiver household, with a child aged 4 to 17 years. Sample selection reflected the focus on work-family conflict and partner-conflict as important predictors of interest. Parent-reported, child negative affect and the negative impact on child functioning since the pandemic started, were the main outcomes of interest. Hierarchal linear regression models were constructed, and each group of predictors (covariates, family factors and COVID-19 experiences) were added in a step-wise fashion. Findings were also stratified by child age and child gender. Missing data were handled via multiple imputations. RESULTS: The final model accounted for 38.7% of the variability in negative affect scores, and 24.1% of the variability in COVID-19 negative impact scores. Negative affect was significantly associated with all family factors, resource-related COVID-19 stressors and positive COVID-19 experiences. The negative impact of COVID-19 on child functioning was significantly associated with all COVID-19 experiences and all family factors except overreactive parenting. Caregiver depression was the strongest predictor of worsening child mental/emotional health in all models. Upon stratifying the analyses by child gender and age, partner-conflict was only a predictor of child mental/emotional health for females and child adolescents. Additionally, health-related COVID-19 stress was a significant predictor for males/other only and caregiver anxiety, overreactive parenting and health-related COVID-19 stressors were significant predictors for children, but not for adolescents. CONCLUSIONS: The pandemic recovery period in Ontario should consist of significant efforts to provide preventative family-based programming and interventions to address the growing mental health crisis in children. Future research efforts should aim to explore the mechanisms by which family factors and COVID-19 specific experiences interact to produce various family dynamics and psychological presentations in children. Further research should also replicate the present study in marginalized and culturally diverse populations.

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)0.002
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 score0.038
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.033
GPT teacher head0.305
Teacher spread0.272 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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