Occupational Therapy’s Role in Assisting with Community Reintegration for Survivors of Human Trafficking
Bibliographic record
Abstract
Victims of human trafficking are exposed to traumatic experiences that affect their physical, psychological, cognitive, and social health. As survivors enter the healing stage and progressed in their journey to community reintegration, the complex traumatic experiences may affect their independence with occupational tasks. This capstone explored the areas where occupational therapy can assist with community reintegration for survivors of human trafficking. Interviews were conducted through casual conversation using the modified Canadian Occupational Therapy Measure, (COPM) as guidance, and the Quality of Life Scale (QOLS) was used to gather baseline data. Survivors identified priorities of their everyday living and the barriers to them achieving their goals: education, vocation, transportation, health, leisure, and healthy relationships. The essential staff working with clients identified the same concerns as clients and concerns for the staff’s emotional and cognitive health due to emotional exposure to client trauma. A program was developed to guide survivors in vocation, education, physical health, and emotional health to promote independence and autonomy. A separate program was developed to guide staff into emotionally supportive conversations and encourage reflective program meetings to reduce the risk of compassion fatigue.
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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 | 0.003 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".