Sources of child maltreatment information in Canada.
Bibliographic record
Abstract
AIM: Interest in understanding the problem of child maltreatment is widely shared by governments, organizations of physicians, and others. Our objective was to describe and discuss sources of information in Canada that could be used to help understand the nature and scope of the problem, either within any province or territory, or across all of Canada. METHODS: A series of web searches and a focused literature review were conducted to identify sources of child maltreatment information. Government departments responsible for child welfare were also contacted on an as-needed basis in order to identify additional sources. RESULTS: Identified sources included: child welfare administrative provincial/territorial data and reports based on those data, other child welfare information, surveys of child protection workers and shelter workers, mortality/morbidity data, police data, direct surveys of children and their parents, and the 2011 Canadian census. Each type of source had strengths and limitations in terms of how it could describe the nature and scope of the problem of child maltreatment. CONCLUSION: Increased use of morbidity and mortality data, data linking, expanding existing databases, and increasing the use of general population surveys could expand understanding of child maltreatment in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.055 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".