Structural stigma within inpatient care for people who inject drugs: implications for harm reduction
Bibliographic record
Abstract
BACKGROUND: Individuals suffering with addiction have historically experienced disproportionally high levels of stigma. The process of inpatient care for those with substance abuse disorder (SUD) is multifaceted, shaped by the interplay of human interactions within the healthcare team and overarching structural factors like policy. While existing literature predominantly addresses personal and interpersonal stigma, the influence of structural stigma on care delivery practices remains understudied. Our research aims to investigate the impact of structural stigma on care processes for individuals with SUD admitted to acute medicine units. METHODS: We conducted a secondary analysis of observation notes and interview transcripts utilizing an analytic framework related to structural stigma adapted from previous research. Data was collected from June 2019 to January 2020 in 2 hospitals. 81 participants consented to observation and 25 to interviews. Interviews were conducted with patients (n = 8), healthcare staff (n = 16), and caregivers (n = 1). RESULTS: Each aspect of care for people with SUD is adversely influenced by structural forms of stigma. There was evidence of a gap in accessing care and time pressures which deteriorated care processes. Structural stigma also manifested in the physical spaces designed for care and the lack of adequate resources available for mental health and addictions care. We found that structural stigma perpetuated other forms of implicit and explicit stigma. CONCLUSIONS: Structural stigma and other forms of stigma are interconnected. Improving care for people with SUD in hospital settings may require addressing structural forms of stigma such as how physical spaces are designed and how mental healthcare is integrated with physical healthcare within inpatient settings.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".