Sex Workers in Canada Face Unequal Access to Healthcare: A Systems Thinking Approach
Bibliographic record
Abstract
OBJECTIVES: Despite Canada's universal health system, sex workers across the country face an alarmingly high number of barriers when they seek to healthcare services. This has resulted in unmet healthcare needs and substantially poorer health outcomes than the general Canadian population. The objective of this study was to use a systems thinking approach to gain an in-depth understanding of the barriers sex workers face and how access could be improved. METHODS: The analysis was conducted using a systems thinking methodology, which incorporates systems tools and inquiry processes. The methodology comprised 2 domains of inquiry: (1) Problem Landscape, (2) Solutions Landscape. Systems tools and methods, such as causal loop diagrams, iceberg diagram, and systems mapping, investigated the problem landscape for understanding the interconnected nature of the issue, alongside review of both published and gray literature. An environmental scan explored the current solutions landscape. These methods connected through systemic inquiry processes, including ongoing review and application of diverse perspectives, boundary judgments, interrelationships; enabled gaps and levers of change to be determined. RESULTS: The main barriers sex workers face are stigma, criminalization, accessibility, and cost of healthcare. The stigma of sex work stems from otherization, paternalism, and moralistic, faith-based beliefs. The barriers unique to sex work are stigma and criminalization; both of which surface as avoidance, dislike, and/or fear of medical professionals. Five gaps each with a lever of change to improve access were identified: (1) Stigma - Collectivization and external collaboration, (2) Criminal status of sex work - Decriminalization, (3) Lack of adequate education - Improved healthcare professional training and anti-discriminatory health policies, (4) Lack of support - Increased community-based healthcare services, (5) Cost of healthcare - Universal coverage of "secondary" healthcare components. CONCLUSION: Through reducing the stigma surrounding sex work, making changes that improve the healthcare services that sex workers receive, and collaboration between involved parties, sex workers can be prevented from falling through the cracks of the Canadian healthcare system; lessening the health inequities sex workers face and improving their health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".