Policing child sexual exploitation and abuse cases: a qualitative PRIORITY study of the challenges faced by law enforcement officers in Germany, Portugal, and Sweden
Bibliographic record
Abstract
This study aimed to explore the primary challenges experienced by European law enforcement officers who deal daily with child sexual exploitation and abuse (CSEA) investigations. We conducted 6 focus group discussions with 26 criminal investigators and managers from Germany (n = 9), Portugal (n = 9), and Sweden (n = 8). Data were collected as part of the PRIORITY project, whose wider goal is to decrease the occurrence of CSEA by providing anonymous online interventions to individuals concerned about their sexual urges towards children. We employed reflexive thematic analysis at the semantic level. By adopting the Barnahus model as a theoretical lens, we generated three recurring themes and 13 related sub-themes. These overarching themes encompassed a range of organisational barriers, investigative difficulties, and systemic shortcomings and were found to extend across national borders, exhibiting both similarities and context-specific variations across the three countries. We discuss our findings in relation to the most significant policy and practice implications, within the context of the Barnahus model implementation. Additionally, our findings lay the groundwork for five empirically based recommendations aimed at enhancing the effectiveness of future law enforcement responses and prevention efforts in regard to child sexual exploitation and abuse cases at a European level.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".