Bibliographic record
Abstract
Human trafficking is a global public health crisis catastrophically threatening the health and well-being of those trafficked. Limited studies measure health care providers’ confidence in their ability to recognize, treat, and refer those being trafficked. This integrative review synthesizes current knowledge on human trafficking and identifies gaps in research on educational interventions aimed at increasing provider knowledge and awareness as well as confidence in treating and referring those being trafficked. A systematic search of five databases identified peer-reviewed published papers between 2015 and 2021. The integrative review followed the framework of Toronto and the systemic search was guided by the Preferred Reporting Items for Systematic Revies and Meta-Analyses (PRISMA). Melnyk’s Levels of Evidence framework was used for appraising the quality of evidence. Findings across studies (N = 11) reveal that providers (nurses, doctors, social workers, and hospital staff) have low knowledge and confidence in their knowledge surrounding human trafficking and their role in identification, treatment, and referrals related to an array of barriers. Further findings across studies (N = 13) reveal that providers’ knowledge and confidence knowledge about human trafficking and identification, and referral of those being trafficked increased significantly with an array of educational interventions, but the transfer of this new knowledge to practice is a gap in research, as few studies reported this (n = 2).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".