Child maltreatment during the COVID-19 pandemic: implications for child and adolescent mental health
Bibliographic record
Abstract
As societies worldwide addressed the numerous challenges associated with the COVID-19 pandemic, a troubling concern emerged-the possible rise of child maltreatment, which is a pernicious risk factor for child and adolescent mental health difficulties. This narrative review aims to provide a comprehensive understanding of how the many changes and challenges associated with the pandemic influenced worldwide occurrences of child maltreatment and, subsequently, the mental health of children and adolescents. First, we present the well-established evidence regarding the impact of child maltreatment on the mental health of children and adolescents both before and during the COVID-19 pandemic. Next, we examine the existing literature on the prevalence of child maltreatment during the pandemic, explanations for conflicting findings, and key mechanisms influencing the prevalence of maltreatment. Using a heuristic model of child maltreatment and its downstream influence on child mental health, we discuss risk and protective factors for maltreatment as well as mechanisms by which maltreatment operates to influence child and adolescent mental health. Finally, based on the accumulated evidence, we provide important recommendations for advancing research on child maltreatment, emphasizing the necessity for routine monitoring of maltreatment exposure at a population level, and discussing the implications for the field of child protection. This comprehensive review aims to contribute to the understanding of the challenges arising from the intersection of the COVID-19 pandemic and child maltreatment, with the goal of informing effective interventions in the domain of child welfare.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".