INNOVATE TO STRENGTHEN MULTIDISCIPLINARY WORK IN CHILD ABUSE: THE CONTRIBUTION OF AN INTEGRATED INFORMATION SYSTEM
Bibliographic record
Abstract
The assessment and care of sexually or physically abused children and their families require the multidisciplinary collaboration of the medical, psychosocial, police and justice sectors.On the initiative of Youth Protection Agency (YPA) and a university hospital in Quebec City, Canada, a new program named Services intégrés en abus et maltraitance (SIAM; Integrated Services in Abuse and Maltreatment) was launched in 2018.Based on the Child Advocacy Centers model, the SIAM provides children, adolescents and their families with on-site integrated clinical assessment, investigation, treatment, support and advocacy services (Alain, Nadeau & al., 2016).The success of child maltreatment interventions requires evaluation and research activities, but especially the integration of available data and expertise (Alain, Clément & al., 2022).Therefore, the multi-agency representatives involved in the SIAM were mobilized to develop, within the SIAM, an innovative information system.This system meets operation, evaluation and research needs with integrated, compatible and continuous clinical and administrative data.This communication aims to: 1) present the main characteristics of the SIAM innovative and Integrated Information System (IIS), as well as the issues associated with its creation and exploitation and 2) present the results of the first exploratory study using this IIS and share potential advances in terms of knowledge and practices.The sample of this exploratory study consists of 1633 situations referred to the SIAM and for which the service trajectory had ended as of December 2021.One of the main advantages of this integrated system is its capacity to follow the entire trajectory of socio-judicial services, from the moment the situation is reported to YPA until the judicial process, including psychosocial and trauma-informed support.Following the initial multidisciplinary triage discussion, 40% (n = 653) of the situations went to the police investigation stage, whereas the other 60% (n = 980) were redirected to YPA only.Charges were laid in 7% (n = 47) of the situations investigated by the police and trauma-informed support services were granted in 4% (n = 73) of all the situations under study (investigated or not).Descriptive and bivariate analyses about the situations and a comparison between the situations in which trauma-informed support services were granted and those without such support will be presented.
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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 | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".