Intersections between syndemic conditions and stages along the continuum of overdose risk among women who inject drugs in Mexicali, Mexico
Bibliographic record
Abstract
BACKGROUND: Research on women who inject drugs is scarce in low- and middle-income countries. Women experience unique harms such as sexism and sexual violence which translate into negative health outcomes. The present work aims to provide insight into the experiences of women who inject drugs at the US-Mexico border to identify social and health-related risk factors for overdose to guide harm reduction interventions across the Global South. METHODS: We recruited 25 women ≥ 18 years of age accessing harm reduction and sexual health services at a non-governmental harm reduction organization, "Verter", in Mexicali, Mexico. We employed purposeful sampling to recruit women who inject drugs who met eligibility criteria. We collected quantitative survey data and in-depth interview data. Analyses of both data sources involved the examination of descriptive statistics and thematic analysis, respectively, and were guided by the syndemic and continuum of overdose risk frameworks. RESULTS: Survey data demonstrated reports of initiating injection drug use at a young age, experiencing homelessness, engaging in sex work, being rejected by family members, experiencing physical violence, injecting in public spaces, and experiencing repeated overdose events. Interview data provided evidence of stigma and discrimination toward women, a lack of safe spaces and support systems, risk of overdose-related harms, sexual violence, and the overall need for harm reduction services. CONCLUSION: Women who inject drugs in Mexicali describe experiences of violence, overdose, and public injecting. Women are particularly vulnerable in the Mexicali context, as this area faces a noticeable lack of health and social services. Evidenced-based harm reduction strategies such as safe consumption sites and overdose prevention strategies (e.g., naloxone distribution and training) may benefit this population. Evidence from local organizations could help close the gap in service provision in low-resource settings like Mexico, where government action is almost nonexistent.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".