A review of recent analyses of the Canadian Incidence Study of Reported Child Abuse and Neglect (CIS)
Bibliographic record
Abstract
INTRODUCTION: The objective of this analysis is to identify, assess the quality and summarize the findings of peer-reviewed articles that used data from the Canadian Incidence Study of Reported Child Abuse and Neglect (CIS) published since November 2011 and data from provincial oversamples of the CIS as well as to illustrate evolving uses of these datasets. METHODS: Articles were identified from the Public Health Agency of Canada's data request records tracking access to CIS data and publications produced from that data. At least two raters independently reviewed and appraised the quality of each article. RESULTS: A total of 32 articles were included. Common strengths of articles included clearly stated research aims, appropriate control variables and analyses, sufficient sample sizes, appropriate conclusions and relevance to practice or policy. Common problem areas of articles included unclear definitions for variables and inclusion criteria of cases. Articles frequently measured the associations between maltreatment, child, caregiver, household and agency/referral characteristics and investigative outcomes such as opening cases for ongoing services and placement. CONCLUSION: Articles using CIS data were rated positively on most quality indicators. Researchers have recently focussed on inadequately studied categories of maltreatment (exposure to intimate partner violence [IPV]), neglect and emotional maltreatment) and examined factors specific to First Nations children. Data from the CIS oversamples have been underutilized. The use of multivariate analysis techniques has increased.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.027 | 0.038 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".