139 Access to health and mental health services for trafficked migrants in Canada: case study of Ontario
Bibliographic record
Abstract
Abstract OP 15: Exclusion and Discrimmination 1, B308 (FCSH), September 3, 2025, 17:00 - 18:00 Background and aims In 2006, Canada introduced an assistance/protection program for trafficked migrants, which allows them to apply for a Temporary Resident Permit (TRP) and access basic healthcare, including counselling. However, little is known about the actual availability and accessibility of healthcare and mental health services for migrants who left trafficking situations. Trafficked migrants have unique needs and face unique challenges due to compound factors of precarious immigration status, language barriers, lack of information and others. The paper investigates: (a) what specialized healthcare, mental health services and programming for trafficked persons are available in Ontario, Canada; (b) whether such services respond to unique needs of migrants post-trafficking and whether they are accessible to them. Methods The paper relies on analysis of anti-human trafficking plans, strategies and policies (parts related to health services), literature review and a survey of community organizations in Ontario, which specialize in counselling and trauma-informed programming for survivors of trafficking. Results There is a gap in policy, research and delivery of supports for trafficked migrants. Most current initiatives focus on domestic sex trafficking and child sexual exploitation, paying little attention to trafficked migrants. Structural issues such as short-term nature of TRPs, processing delays and uncertainly about longer-term status in Canada exacerbate barriers as without legal immigration status, survivors cannot access health/support services and are at risk of removal. Conclusion The current gaps must be addressed: (a) at the research level by studying the healthcare needs of trafficked migrants, their experiences in Ontario healthcare system, developing tools for service providers to better understand and identify trafficking situations; (b) at the policy level by revising Ontario anti-human trafficking strategy to address trafficked migrants; (c) at service-provider level by supporting initiatives that respond to intersecting needs of trafficking migrants such as legal advice, language services, healthcare, counselling and housing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".