The process of safer crack use amongst women in Vancouver's downtown eastside
Bibliographic record
Abstract
Crack cocaine is prevalent in Vancouver’s Downtown Eastside, with evidence suggesting women use more than men. Crack cocaine poses many harms to the body, and women face unique harms due to the gendered use of crack. However, there has been little investigation into how women go about minimizing some of the harms associated with crack. Informed by harm reduction and women’s-centred philosophies, a grounded theory approach was employed to explore the process that women engage in to limit the physical, psychological and interpersonal harms associated with crack use, as well as identify the social, economic and political factors that influence the process of safer use. Data were collected via seven group interviews (n=27) that took place over a three month period with women who were actively using crack cocaine. Data illustrated women’s crack use patterns shifted over time from heavier to more intermittent use, and four central processes that enabled women to practice safer crack use were identified. At the root of these processes was a dedication to care for the self and others. The processes were identified as: establishing a safe physical space, building trusting relationships, learning about safer crack use, and accessing safer use equipment. These strategies were in turn influenced by larger contextual factors including the spatial environment (violence and police activity), economics (living with extreme financial limitations) and politics (the instability of supportive housing and lack of safe places for women). Women demonstrated proficiency to care for themselves and others in the context of crack use, but many changes within the political and health care systems are necessary to facilitate safer practices to improve health outcomes. Firstly, a political agenda that is dedicated to the development of supportive housing is essential for safer use, as is greater access to income assistance. Furthermore, harm reduction programming that focuses on women’s contributions and expertise in the realm of safer use is essential to ongoing development of a supportive community of women. Moreover, the availability of safer use equipment is quintessential for women to apply knowledge regarding safer crack use to minimize some of the harms associated with crack.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".