Pandemic-related challenges accessing food and primary healthcare among sex workers during the COVID-19 pandemic: findings from a community-based cohort in Vancouver, Canada
Bibliographic record
Abstract
INTRODUCTION: Globally, the COVID-19 pandemic upended healthcare services and created economic vulnerability for many. Criminalization of sex work meant sex workers were largely ineligible for Canada's government-based financial pandemic relief, the Canadian Emergency Response Benefit. Sex workers' loss of income and inability to access financial support services during the pandemic resulted in many unable to pay rent or mortgage, and in need of assistance with basic needs items including food. Little is known about the unique experiences of sex workers who faced challenges in accessing food during the pandemic and its impact on healthcare access. Thus, we aimed to identify the association between pandemic-related challenges accessing food and primary healthcare among sex workers. METHODS: Prospective data were drawn from a cohort of women sex workers in Vancouver, Canada (An Evaluation of Sex Workers' Health Access, AESHA; 2010-present). Data were collected via questionnaires administered bi-annually from October 2020-August 2021. We used univariate and multivariable logistic regression with generalized estimating equations to assess the association between pandemic-related challenges accessing food and challenges accessing primary healthcare over the study period. RESULTS: Of 170 participants, 41% experienced pandemic-related challenges in accessing food and 26% reported challenges accessing healthcare. Median age was 45 years (IQR:36-53), 56% were of Indigenous ancestry, 86% experienced intimate partner violence in the last six months, and 62% reported non-injection substance use in the last six months. Experiencing pandemic-related challenges accessing food was positively associated with challenges accessing primary healthcare (Adjusted Odds Ratio: 1.99, 95% Confidence Interval: 1.02-3.88) after adjustment for confounders. CONCLUSIONS: Findings provide insight about the potential role community-based healthcare delivery settings (e.g., community clinics) can play in ameliorating access to basic needs such as food among those who are highly marginalized. Future pandemic response efforts should also take the most marginalized populations' needs into consideration by establishing strategies to ensure continuity of essential services providing food and other basic needs. Lastly, policies are needed establishing basic income support and improve access to food resources for marginalized women in times of crisis.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".