Health consequences of child removal among Indigenous and non‐Indigenous sex workers: Examining trajectories, mechanisms and resiliencies
Bibliographic record
Abstract
The child protection system can be a highly consequential institution for mothers who are sex workers, yet scant attention has been paid to the health consequences of its policies on this population. Drawing on 31 in-depth, semi-structured interviews with 19 Indigenous and 12 non-Indigenous sex workers in Vancouver, Canada, and using the stress process model and the concept of slow violence, this study proposes a typology of four trajectories through which child removal by this system shaped sex workers' health. Results suggest that child removal has health consequences beyond the conventionally thought of mechanism of mental distress and related health sequelae, to additionally alter women's social conditions, which also carried risks for health. Notably, while trajectories of Indigenous and non-Indigenous sex workers were similar, Indigenous participants, whose families are disproportionately impacted by long-standing colonial policies of child removal, were more severely jeopardized. Findings highlight how child removal can enact violence in the form of reverberating harms to sex workers' health, further reinforcing their marginalized statuses. This study calls for greater attention to how the child protection system (CPS) may influence the health of marginalized mothers, including how health inequities may be both causes and consequences of interventions by this system.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".