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Record W4307493287 · doi:10.1097/acm.0000000000004879

Human Trafficking: Addressing the Chiasmic Training Gap Through a Train-the-Trainers Model

2022· article· en· W4307493287 on OpenAlexaboutno aff
Hanni Stoklosa, Rahel Bosson, Sue Farrell

Bibliographic record

VenueAcademic Medicine · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careExperiential learningCurriculumConfidentialityWorkforceMedicineNursingMedical educationPsychologyPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.009
Scholarly communication0.0040.006
Open science0.0040.014
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0140.002

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.

Opus teacher head0.269
GPT teacher head0.423
Teacher spread0.154 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations3
Published2022
Admission routes1
Has abstractyes

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