Child Maltreatment Fatality Review: Purposes, Processes, Outcomes, and Challenges
Bibliographic record
Abstract
Better understanding of the causes and circumstances of maltreatment deaths of children is needed to prevent tragedy. The purpose of this article is to facilitate understanding of child maltreatment fatality review processes and their outcomes. A literature review was conducted through searches of the databases PubMed, PsycINFO, and EMBASE and through citations in publications. Over 165 publications were reviewed and 55 were selected for inclusion. Papers were from the United States, England, Ireland, Northern Ireland, Netherlands, France, Canada, Australia, South Africa, Switzerland, Saudi Arabia, Japan, and China. These were included if they described fatality review goals, authority, procedures, and outcomes. Although we searched databases on a continual basis during the preparation of this review, we could have missed publications, particularly those in newspapers and journals that are not included in large-scale databases or cited in other articles. Improvement of fatality review requires diligence by individuals and organizations that provide information to the reviewers. Among challenges to the review process are varying criteria for review, misclassifications of the manner of death, inadequate or incomplete forensic and medical investigations, lack of information about the perpetrator, diversity of the community, concealment of the cause of death by parents or other caregivers, and disagreement among reviewers about the results of their inquiries. Institutional challenges are also present, which include the need for funding, privacy issues on obtaining information, updating reviewer training, lack of follow-up by institutional authorities on the recommendations of the reviews, and research facilitating the review of maltreatment fatalities.
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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 | 0.356 | 0.696 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.043 | 0.032 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".