Sex workers’ engagement with mutual aid on-the-ground and online: Exploring access to grassroots support networks among a community-based cohort in Vancouver, Canada (2020–2024)
Bibliographic record
Abstract
Mutual aid is the practice of grassroots support based on principles of direct action and non-hierarchical cooperation, central to the health and well-being of marginalized communities including sex workers. Despite mutual aid's centrality to sex workers' well-being, there is a dearth of health research on sex workers' uptake of mutual aid, particularly digital modes, and its relationship to supportive occupational conditions. Drawing on longitudinal cohort data, we measured recent mutual aid and its association with structural and occupational conditions among 900+ sex workers in Metro Vancouver, Canada. Informed by mutual aid principles and a structural determinants framework, we examined uptake of "digital" and "on-the-ground" (i.e., in-person) mutual aid and explored associations with occupational conditions in a community-based cohort of sex workers over four years (2020-24). Among 367 sex workers, 37.2 % and 58 % reported engaging in digital and on-the-ground mutual aid, respectively. We found higher odds of utilizing "on-the-ground" mutual aid among those experiencing recent physical/sexual violence and lower odds of digital mutual aid among sex workers who experienced lifetime incarceration. The findings affirm engagement with mutual aid as a critical support model for sex workers, while highlighting barriers to emerging digital modalities. There is need for full decriminalization of sex work and the democratization of digital tools to reduce barriers to essential resources and support networks. Further, our findings underscore the potential of mutual aid principles within public health, by learning from communities who have cultivated grassroots models of care in the context of structural exclusion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".