Prevention of Child Maltreatment: Commentary on Eckenrode, MacMillan and Wolfe
Bibliographic record
Abstract
Introduction Child maltreatment affects more than one million American and Canadian children annually. That figure, based largely upon reports from child protective services, is widely regarded as seriously under-representing the actual scope of the problem because in population-based surveys about one-third of adults report having been abused as children. Neglect is more prevalent than abuse, but can be a precursor to abuse. Child maltreatment has negative consequences that reach beyond the immediate pain of childhood victimization. Dollar costs to society are great, and there are significant and serious lifetime mental and physical sequelae to victims, such as major depression and cardiovascular disease. John Eckenrode, Harriet MacMillan and David Wolfe have stressed the utility of the developmental-ecological and public-health models in preventing child maltreatment. In doing so, they note the need to identify effective prevention programs that address child maltreatment at multiple levels, including family, schools, the health-care system and the community. More and better surveillance and etiologic data and the establishment of attainable programmatic goals are further concerns of these authors.
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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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.057 | 0.069 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".