Child Well‐Being and Family Quality of Life During the COVID‐19 Pandemic
Bibliographic record
Abstract
BACKGROUND: Little is known about changes in child well-being and family quality of life (QoL) among children seeking emergency department care because of mental health concerns over the course of the pandemic. METHODS: Prospective cohort study of children < 18 who visited two paediatric EDs in Alberta, Canada, for an acute mental health concern. Early and late pandemic time periods were defined as 15 March 2020-14 March 2021 and 1 July 2021-30 June 2022, respectively. The Stirling Children's Well-being and Warwick-Edinburgh Mental Well-being scales quantified well-being; the Family Quality of Life Scale assessed family QoL. These scales were completed as soon as possible following the ED visit. Linear regression models assessed the association between pandemic period and the change in well-being and family QoL. RESULTS: One thousand four hundred four children were enrolled during the study time periods (50.4% early, 49.6% late). Seventy-two percent (1009/1404) of participants were White, 53.8% (744/1404) were female, and the median age was 13 (IQR, 11-15) years. Well-being remained unchanged between time periods, whereas family QoL was lower in the late pandemic time period than in the early period (mean difference: -2.16, 95% CI: -3.79, -0.53; p = 0.01). Among children < 13 years, previous mental health care and requiring inpatient admission were negatively associated with well-being. Having an autism diagnosis and a comprehensive ED mental health evaluation were negatively associated with well-being in older children. CONCLUSIONS: Reduced family QoL may signal stressors experienced by caregivers and the lingering consequences of the pandemic. Resources that address well-being and support the family unit are needed to improve the mental health of children.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".