The Characteristics of Abused Women in the Caseload of a Child Protection Service1
Bibliographic record
Abstract
This study identifies case characteristics of abused women in a child protection services caseload. The sample was drawn from a large child protection agency in southwestern Ontario consisting of 853 children chosen at random from among all children who had a new referral over a 12-month period. Files of the children were reviewed to derive study data. Results indicate that abused women are significantly more likely to have mental and physical health problem characteristics such as substance abuse, mental illness, impaired mental/emotional/intellectual capacity to care for the child, and a chronic medical condition compared to non-abused women. Abused women are more likely to rely on social assistance, be unemployed, less likely to cope effectively with family stress, and less likely to have the availability of reliable and useful social supports in place compared to non-abused women. Abused women are more likely to have been abused and neglected as children and more likely to have been involved with child protection services as children than are women who were not abused. Children of abused women are more likely to be currently in the care of child protection authorities than are the children of women who are not abused. Surprisingly, violence perpetrated by the mother towards their child and the severity of child abuse/neglect inflicted by the mother does not differ for children of abused and non-abused women. These findings are discussed in relation to their implications for understanding woman abuse in the context of child protection services.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".