Using Theory and Technology to Build an Interprofessional Community of Human Trafficking Educators
Bibliographic record
Abstract
INTRODUCTION: Human trafficking, a global health crisis, requires interprofessional responses. Skilled teachers are needed to train health care providers about human trafficking. METHODS: To promote nonhierarchical interprofessional collaborative learning, we applied social cognitive and experiential learning theories within a dialectical constructivist program design to scaffold participants' knowledge and leveraged technology to build and sustain the program's community. WhatsApp and Flipgrid connected participants and faculty prior to the program. Participants' reflections on experiences were used to inform 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 used learning theories to cocreate and teach about labor and sex trafficking, disclosure, and the law. A reciprocal teaching activity facilitated participants' integration of new knowledge with authentic work responsibilities. Constructive peer feedback on the content, clarity, and engagement of their teaching reinforced participants' self-efficacy in expanding their education work in their home organizations. RESULTS: As of 2021, 156 physicians, nurses, social workers, advanced practice providers, psychologists, and public health workers, from the United States, United Kingdom, Canada, and Trinidad/Tobago, have graduated from the program. Three-month postprogram surveys indicated lasting knowledge and skills changes in use of the Stop, Observe, Ask, Refer framework, teaching with adult learning principles, and creating organizational trafficking protocols. CONCLUSION: The strategic application of learning theory and technology has enabled us to foster a nonhierarchical community of interprofessional learners, cultivating a dynamic network of educators who continue to make international impacts on people with an experience of human trafficking.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".