Child Physical Abuse With and Without Other Forms of Maltreatment: Dysfunctionality Versus Dysnormality
Bibliographic record
Abstract
The fact that most etiological studies of physical abuse have not taken into account co-occurrence of different forms of maltreatment calls into question the validity of our knowledge on the subject. The aim of this study, therefore, is to compare the etiological patterns of cases of physical abuse reported to Quebec child protective services (CPS) according to whether the abuse occurs alone or co-occurs with other forms of maltreatment. The data are taken from the Quebec Incidence Study (QIS), which examined 4,929 reports investigated by Quebec CPS in the fall of 1998. The cases included 514 children who were physically abused: 269 of them were not subjected to any other type of maltreatment and 245 were also victims of one or two other forms of maltreatment. The survey form provided information on more than 30 characteristics of the children reported, their families, and their parental figures. Bivariate and direct logistic regression analyses revealed that the profile of physical abuse cases varies depending on whether the physical abuse occurs alone (what we are calling dysnormality) or in combination with one or two other forms of maltreatment (dysfunctionality). Those results will help deepen our etiological knowledge of physical abuse and may serve to inspire different types of intervention for the two groups of 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".