Profiles of Child Internalizing and Externalizing Problems During the COVID-19 Pandemic in Jordan and Differences in Mothers’ Psychosocial Functioning
Bibliographic record
Abstract
The COVID-19 pandemic upended children’s lives worldwide, with severe effects on low-income families. Longitudinal studies on child mental health trajectories during crisis periods are scarce, in particular in the Arab region. This study contributes knowledge about children’s mental health and helps to identify children at risk of mental health problems. We explored patterns of change in Jordanian preschool-aged children’s externalizing and internalizing problems during the COVID-19 pandemic and examined associations with mothers’ psychosocial functioning. Fifty mothers (38% from low-income families) reported on their child’s mental health in 2019 and three times during the pandemic (June 2020, December 2020, and June 2021). In June 2021, mothers also reported on their own psychological functioning. Using a longitudinal k-means clustering algorithm, we identified three internalizing problem profiles (low and stable, moderate and stable, high and increasing) and three externalizing problem profiles (low and stable, moderate and decreasing, high and stable). Externalizing problem profiles differed with regard to child sex (F [2,47] = 3.20, P = 0.050, η2 = 0.12). Furthermore, externalizing problem profiles differed in relation to mothers’ depressive symptoms (F [2,42] = 3.62, P = 0.04, η2 = 0.15). We found that young children from Jordan responded differently to the stressors of the COVID-19 pandemic. This heterogeneity can inform interventions targeting vulnerable 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".