Social service providers’ perspectives on caring for structurally vulnerable hospital patients who use drugs: a qualitative study
Bibliographic record
Abstract
BACKGROUND: People who use drugs and are structurally vulnerable (e.g., experiencing unstable and/or lack of housing) frequently access acute care. However, acute care systems and providers may not be able to effectively address social needs during hospitalization. Our objectives were to: 1) explore social service providers' perspectives on addressing social needs for this patient population; and 2) identify what possible strategies social service providers suggest for improving patient care. METHODS: We completed 18 semi-structured interviews with social service providers (e.g., social workers, transition coordinators, peer support workers) at a large, urban acute care hospital in Western Canada between August 8, 2018 and January 24, 2019. Interviews explored staff experiences providing social services to structurally vulnerable patients who use drugs, as well as continuity between hospital and community social services. We conducted latent content analysis and organized our findings in relation to the socioecological model. RESULTS: Tensions emerged on how participants viewed patient-level barriers to addressing social needs. Some providers blamed poor outcomes on perceived patient deficits, while others emphasized structural factors that impede patients' ability to secure social services. Within the hospital, some participants felt that acute care was not an appropriate location to address social needs, but most felt that hospitalization affords a unique opportunity to build relationships with structurally vulnerable patients. Participants described how a lack of housing and financial supports for people who use drugs in the community limited successful social service provision in acute care. They identified potential policy solutions, such as establishing housing supports that concurrently address medical, income, and substance use needs. CONCLUSIONS: Broad policy changes are required to improve care for structurally vulnerable patients who use drugs, including: 1) ending acute care's ambivalence towards social services; 2) addressing multi-level gaps in housing and financial support; 3) implementing hospital-based Housing First teams; and, 4) offering sub-acute care with integrated substance use management.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".