“Locked in a Jail Cell in Your Own Home”: Child Maltreatment Investigators’ Perspectives of COVID-19’s Effects on Maltreated Children
Bibliographic record
Abstract
Children were at a greater risk of adverse experiences, including maltreatment, during the COVID-19 pandemic given the increased stress experienced by families and reduced visibility outside the home. Child maltreatment investigators witnessed the effects of the pandemic on maltreated children and offer valuable insight regarding children's experiences during the pandemic. The objective of this study was to examine child maltreatment investigators' perspectives of the impact of the COVID-19 pandemic on maltreated children and their families in Canada. Sixteen child maltreatment investigators were recruited from agencies across Canada that investigate or offer services to children suspected of having been maltreated. Three focus groups were conducted, which followed a semi-structured interview guide developed by the researchers. Thematic analysis resulted in five primary themes regarding maltreatment investigators' perceptions of the pandemic's effects on children, including child maltreatment during the COVID-19 pandemic, increased exposure to violent and traumatic events, stress and challenges faced by families, reduced access to services, and challenges and delays with maltreatment investigations. Child maltreatment investigators perceived that the pandemic profoundly impacted maltreated children and their families. It is critical to ensure children and parents have access to services during future emergencies.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.018 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| 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".