The Experience of Violence Against Women Who Use Injection Drugs: An Exploratory Qualitative Study
Bibliographic record
Abstract
BACKGROUND: While literature exists about persons who use injection drugs, few studies explore the experience of women who use these substances. Furthermore, even less research specifically focuses on the lives and experiences of homeless women who use injection drugs. What literature does exist, moreover, is often dated and primarily addresses concerns about infectious disease transmission among these women; and some highlight that these women have lives fraught with violence. PURPOSE: To update this knowledge and better understand the lives of women who use injection drugs in the Canadian context. METHODS: We undertook an exploratory qualitative study and we engaged in semi-structured interviews with 31 homeless women who use injection drugs in downtown Ottawa, Canada. We analyzed the data using the principles of applied thematic analysis. RESULTS: Our data identified that violence pervaded the lives of our participants and that these experiences of violence could be categorized into three main areas: early and lifelong experiences of violence; violence with authority figures (e.g., police, healthcare); and societal violence toward women who use injection drugs. CONCLUSIONS: We take these findings to mean that, violence toward women is rampant in Canada (not just internationally) and that healthcare workers play a role in propagating and addressing this violence.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".