Social and structural determinants of injection drug use‐associated bacterial and fungal infections: A qualitative systematic review and thematic synthesis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Injection drug use-associated bacterial and fungal infections are increasingly common, and social contexts shape individuals' injecting practices and treatment experiences. We sought to synthesize qualitative studies of social-structural factors influencing incidence and treatment of injecting-related infections. METHODS: We searched PubMed, EMBASE, Scopus, CINAHL and PsycINFO from 1 January 2000 to 18 February 2021. Informed by Rhodes' 'risk environment' framework, we performed thematic synthesis in three stages: (1) line-by-line coding; (2) organizing codes into descriptive themes, reflecting interpretations of study authors; and (3) consolidating descriptive themes into conceptual categories to identify higher-order analytical themes. RESULTS: We screened 4841 abstracts and included 26 qualitative studies on experiences of injecting-related bacterial and fungal infections. We identified six descriptive themes organized into two analytical themes. The first analytical theme, social production of risk, considered macro-environmental influences. Four descriptive themes highlighted pathways through which this occurs: (1) unregulated drug supply, leading to poor drug quality and solubility; (2) unsafe spaces, influenced by policing practices and insecure housing; (3) health-care policies and practices, leading to negative experiences that discourage access to care; and (4) restrictions on harm reduction programmes, including structural barriers to effective service provision. The second analytical theme, practices of care among people who use drugs, addressed protective strategies that people employ within infection risk environments. Associated descriptive themes were: (5) mutual care, including assisted-injecting and sharing sterile equipment; and (6) self-care, including vein health and self-treatment. Within constraining risk environments, some protective strategies for bacterial infections precipitated other health risks (e.g. HIV transmission). CONCLUSIONS: Injecting-related bacterial and fungal infections are shaped by modifiable social-structural factors, including poor quality unregulated drugs, criminalization and policing enforcement, insufficient housing, limited harm reduction services and harmful health-care practices. People who inject drugs navigate these barriers while attempting to protect themselves and their community.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".