Gaps in health research related to sex work: an analysis of Canadian health research funding
Bibliographic record
Abstract
Public health is tasked with addressing the urgent global priority of promoting the health and human rights of adults engaged in sex work and research is critical to support this endeavor. ‘What’ is studied, ‘how’ research is done, and ‘who’ is centered in this research is reflected in how research funding is allocated. In this article, we interrogate funding allocation for sex work-related health research in Canada. Drawing on critical perspectives aimed at illustrating how stigma operates in society, we examine operating grants (N = 64) awarded by the federal health research funding agency between 2003 and 2020. We find that sex workers’ health is problematized disproportionately in a street marketplace context that centers on HIV and sexually transmitted infections. Limited work attends to the socio-structural context of sex work and instead perpetuates stigmatizing narratives about sex work. Public health intervention studies are rare, presenting a barrier for implementing and evaluating evidence-based health promotion strategies. Notably, the research projects were conducted by a small number of highly networked, geographically clustered researchers, illustrating gaps in research that considers the complexity of sex work. We propose that it is essential to consider funding as a process that may be limiting the range of health issues being addressed and privileging a small community of researchers, which can inadvertently serve to worsen health inequities among some sex work communities. Researchers and funding bodies may draw on this analysis to inform a research agenda that meaningfully supports the health, safety, and well-being of sex workers in Canada and globally.
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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 | 0.067 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.025 | 0.064 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".