Impacts of the COVID-19 Public Health Crisis on Caring for Sex-Trafficked Persons
Bibliographic record
Abstract
BACKGROUND: Sex trafficking of persons, a pervasive public health issue disproportionately affecting the most marginalized within society, often leads to health as well as social consequences. Social service provision to meet the resulting needs is critical, however, little is known about the current pandemic's impact on providers' capacity to deliver requisite care. METHOD: To examine social service providers' perspectives of care provision for domestically sex-trafficked persons in Ontario, Canada, during the COVID-19 pandemic, we conducted semi-structured interviews with 15 providers and analyzed these using Braun and Clarke's analytic framework. RESULTS: Impacts of the COVID-19 pandemic on social service care provision were connected to individuals' increased vulnerability to trafficking, difficulties safely and effectively providing services to sex-trafficked persons amid pandemic restrictions, and reduction in in-person educational activities to improve providers' capacity to serve this client population. Securing safe shelter was particularly difficult and inappropriate placements could at times lead to further trafficking. CONCLUSION: The pandemic created novel barriers to supporting sex-trafficked persons; managing these sometimes led to new and complex issues. Future efforts should focus on developing constructive strategies to support sex-trafficked persons' unique needs during public health crises.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".