Gap Analysis of Services for Victims and Survivors of Online Child Sexual Exploitation and Abuse in Canada
Bibliographic record
Abstract
This report presents the findings of a gap analysis of specialized services for victims and survivors of online child sexual exploitation and abuse (CSEA) in Canada. The analysis took place between March and July 2019 and was conducted to identify existing specialized support services for victims of CSEA, their families, and adult survivors of CSEA, as well as gaps in the availability of such services, any promising practices in Canada or elsewhere, and challenges to providing specialized supports for these victims and their families. Very few support services and treatment practices are specifically related to online CSEA in Canada. Overall, the most promising practices currently offered in Canada are those that address child victims (children and youth up to the age of 18 years) of crime more generally, especially the services provided by the Child and Youth Advocacy Centre network that is expanding across the country, as well as those provided by the longstanding network of service providers that responds to victims of sexual assault. These networks have great potential to provide specialized supports for online CSEA victims and their families. However, they currently lack expertise in online CSEA, and need evidence-based guidance on how to best support these victims; methods demonstrated to be effective for sexual crime victims in general might not be effective or may even be harmful to victims of online crimes. What is needed is investment in creating the evidence base – that is, data evaluating and verifying the appropriateness and effectiveness of specific support services and approaches to treatment for online CSEA – that would then inform best practices. These best practices could then be conveyed in training for service providers, and integrated into supervision and mentorship structures within organizations. Many of the individuals interviewed for this report are experts in the field of child sexual abuse. Other participants are well versed in providing referrals for clients needing specialized supports for child victims of sexual abuse. However, they did not believe that they had expertise specific to working with victims of online CSEA, and generally did not know of specialized services to which they could refer victims. The extensive and well-established networks of experts in the fields of sexual abuse and child sexual abuse should be mobilized to participate in, and contribute to, research in this area in order to develop evidence-based effective responses for victims of online CSEA.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".