Review of the Internet Child Exploitation (ICE) Counselling Program in Ontario
Bibliographic record
Abstract
This report presents the findings of a review of the Internet Child Exploitation (ICE) Counselling Program, which is funded by the Ontario Ministry of the Attorney General and facilitated by Boost Child & Youth Advocacy Centre (Boost CYAC). The ICE Counselling Program provides referral and funding for short-term counselling for victims of online child sexual exploitation (CSE) and their non-offending impacted family members. This review took place between May and October 2021. Former clients, their impacted family members (IFMs), counsellors, and ICE Counselling Program administrators took part in virtual interviews that sought to understand the value and challenges of this program. Boost CYAC is a pioneer in fulfilling its role as facilitator of the only program of its kind in Canada: the report aims to learn from the experiences of clients, counsellors, and administrators to understand the impact of the ICE Counselling Program and gather suggestions and recommendations. Through semi-structured interviews, all participants were encouraged to reflect on both the value and any challenges that they may have experienced while receiving or providing services through the program. There was overwhelming support for the ICE Counselling Program from all review participants. The opportunity to address the unique harms that are experienced because of online CSE was a considerable value provided to victims and their families. Additionally, the minimal wait time between referral and connection to a counsellor was praised by most participants, as the wait time for other children’s mental health services in Ontario is considerably longer. Counsellors, administrators, and IFMs also highlighted the importance of providing funding and counselling services to caregivers and other members of a victim’s family, as it validated the significance of the impact that this type of victimization has on the family unit. Suggestions for addressing challenges included the following: improving awareness about the existence and scope of the ICE Counselling Program; requiring counsellors to work within a trauma-informed framework; providing specialized clinical training and supervision specific to counselling victims of online CSE; providing optional psychoeducation and orientation to new clients and their IFMs; enhancing funding for victims and families that need it; providing a choice of virtual or in-person counselling; increasing administrative infrastructure; and improving invoicing systems. All participants advocated for the establishment of the ICE Counselling Program model in other jurisdictions, given the strong need and clear value, and based on the findings, the review provided a detailed, rich description of the many aspects of the ICE Counselling Program that work well and also where there is room for growth, from which we were able to produce recommendations for how the program could be improved with a view toward establishing best practices. With this aspirational goal in mind researchers conclude that investing in professional development and clinical supervision for ICE counsellors is one important means to achieve the best possible outcomes for online CSE victims and IFMs, thereby facilitating the development of a specialized community of practice, able to provide specific trauma counselling to victims of online CSE. Such a professional network could foster the further development of an evidence base, currently lacking in both the academic and clinical literature, that would lead to establishing and confirming best practices. A strong professional support network could further serve as a means to recruit and retain counsellors to this challenging area of practice.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.027 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".