Child Sexual Abuse Images Online: Implications for Social Work Training and Practice
Bibliographic record
Abstract
The phenomenon of child sexual abuse images online (CSAIO) presents new and daunting challenges for social workers who work in the field of child sexual abuse (CSA), particularly in relation to assessment and treatment approaches. This paper reports on a grounded theory study that examined the views of CSA practitioners about online abuse images. In-depth qualitative interviews were conducted with fourteen social work practitioners and other helping professionals in child protection and CSA treatment services from Ontario, Canada, to explore their perspectives about effective assessment and treatment for the children in the online images. All participants felt inadequately prepared in terms of their training and experience to effectively respond to these children, particularly regarding the perceived permanence of the abuse images distributed online and their global accessibility. Implications for social work training and practice are provided and the paper concludes with a call for the recognition of CSAIO as a new area in social work practice requiring additional research and specialised training.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.025 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".