The Safe Harbors Youth Intervention Project: inter-sectoral collaboration to address sexual exploitation in Minnesota
Bibliographic record
Abstract
The authors aimed to evaluate the Safe Harbors Youth Intervention Project inter-sectoral collaboration to improve continuity and appropriateness of services for sexually exploited children and adolescents. The study was carried on through an intensive, single case study, drawing on interviews and focus groups with experiential youths (n=125) and multi-sectoral stakeholders (n=196), documented activities, and repeated interviews with collaborating team members (n=29), teen clients (n=46) and parents (n=22). The collaboration was designed around an eight-step process for creating victim-centered protocols within and across organizations, altering services to bridge gaps in care, and creating training tools for the different sectors. The results of the study showed an initial needs assessment documented fragmented care and problematic communication across departments and sectors. The shared protocol development among decision makers from each agency, focused on best practices and evidence-based interventions, fostered trusting relationships, improved awareness of different roles and services, and speeded practice changes to remove barriers to care for sexually exploited youths. A task-focused collaboration with a shared community-wide protocol, increases transparency between services, and ongoing inter-sectoral training helps healthcare team foster a meaningful response to sexually exploited youths.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".