Human Trafficking: Addressing the Chiasmic Training Gap Through a Train-the-Trainers Model
Bibliographic record
Abstract
Human trafficking is a global health problem, affecting the health of men, women, and children, and disproportionately affecting marginalized populations. At some point during their exploitation, those who experience trafficking will likely have contact with a health professional. 1 Unfortunately, many trafficking survivors report that contact with health professionals did not lead to safety and healing: their exploitation was not identified, they felt judged, or they feared arrest or/deportation. A holistic response to trafficking in the health care setting is critical, involving multidisciplinary coordination including social work, nursing, midlevel providers, and physicians. 2,3 The health care workforce is comprised of millions of practicing clinicians who have not been trained on responses to trafficking. Moreover, trafficking is not routinely a part of medical curricula. 4 Given the fact that trafficking is just beginning to come onto medicine’s radar, there are very few individuals with the combination of expertise in teaching skills and trauma-informed clinical trafficking approaches. 4 Simply put, there is a chiasmic gap between those health professionals who are and are not competent in caring for trafficked people and few people qualified to train them. 4 In 2019, 2020, and 2021, we conducted human trafficking health professional “train the trainers” for over 150 teachers. To promote a nonhierarchical environment of interprofessional learning, we used social cognitive constructivism, and experiential learning theory to scaffold participants’ knowledge. We leveraged technology to build community. Whatsapp and flipgrid connected participants and faculty before the program. Participants’ prior experiences informed confidential and respectful information sharing. Live case presentations were interwoven with prerecorded didactics, Zoom break-out case analyses, and Q&A sessions with trafficking survivors. Participants co-developed a didactic in groups for educating others about labor and sex trafficking, disclosure, and the law, facilitating integration of new knowledge with participants’ prior experiences and authentic work responsibilities. Groups then taught their didactics using newly acquired understanding of learning theory and received peer feedback on content, clarity, and engagement of their teaching. Pre–post retrospective surveys assessed changes in knowledge and skills, immediately and 3 months post program. As of 2021, over 150 physicians, medical students, nurses, social workers, public health workers, physician assistants, and psychologists from the United States, United Kingdom, Canada, and Trinidad/Tobago have graduated from the program. Three-month postprogram surveys indicated lasting, statistically significant behavior change in use of the SOAR framework, teaching with adult learning principles, and creating organizational trafficking protocols. Qualitative analysis revealed participants’ training in their communities has led to increased identification of patients experiencing trafficking. Our human trafficking health care “train-the-trainer” model empowers teachers to train others, improve their health systems, and community responses to trafficking. Our human trafficking health care “train-the-trainer” model can be scaled and implemented in regions around the United States to bridge the chiasmic gap between those health professionals who are and are not competent in caring for trafficked people and those qualified to train them.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".