Preventing drug-related deaths in Scotland: perceptions and experiences of engagement in a “shared care” model of service delivery
Bibliographic record
Abstract
Purpose Scotland faces a crisis of drug-related deaths, disproportionately affecting people living in the most deprived areas. The purpose of this paper is to explore patient and service provider perceptions of engagement within shared care treatment systems, acknowledged as a critical factor in preventing drug-related harms and deaths. Design/methodology/approach A qualitative case study approach was adopted, focusing on two primary care practices in highly deprived urban areas. Thematic analysis was used to investigate the interplay of individual, organisational and structural factors acting as facilitators and barriers to service engagement. Data were collected through 34 semi-structured interviews with 6 people who use drugs, 4 family members, 20 health-care practitioners and 4 policymakers. Findings Engagement challenges were multifaceted, encompassing relational aspects (e.g. trust and stigma) and systemic issues, including poor collaboration across professional groups, fragmented services, inadequate communication and resource constraints. Participants emphasised the cumulative impact of socioeconomic deprivation and structural inequalities, which shaped the environments in which drug use occurred and constrained effective care delivery. Practitioners used various strategies, including harm reduction approaches and personalised support, to enhance engagement. Originality/value This paper provides new insights into the challenges faced by practitioners, people who use drugs and families in navigating the shared care system. The findings of this study highlight the need for policy action to strengthen service provision as well as reinforcing the importance of tackling cumulative health and social inequalities, seen as a key factor in drug-related deaths.
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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".