Care professionals’ accounts of providing support and treatment for people with co-occurring alcohol use disorder and depression in the North East of England, UK: A qualitative study informed by complexity theory
Bibliographic record
Abstract
INTRODUCTION: There is an acknowledged care gap for patients with co-occurring substance use and mental ill-health. This study sought to use complexity theory to help make sense of the experiences of people who deliver or commission formal care for patients with alcohol use disorder and depression across one specific health and social care system. METHOD: Qualitative interviews with 26 health and social care professionals in the North East and North Cumbria Integrated Care System, England, were conducted. Data analysis was undertaken using reflexive thematic analysis and informed by key concepts from complexity theory. RESULTS: Three main themes were identified: (1) how the interplay between risk, stigma and resource pressures influences how care professionals interpret and apply practice guidelines; (2) how individualised and disjointed practices have structural and historical roots, in particular the impact of health service commissioning cycles; (3) ways in which practitioners have been able to adapt and engage in creative practice to temporarily plug gaps in care. CONCLUSIONS: The pressure of working with increasingly scarce resources, within a highly fragmented, shifting, and risk-averse care infrastructure, adversely affected professionals' capacity to provide consistent, patient-centered support. Innovations have emerged that address some of these barriers, but further investment is needed to better support the substance use and mental health workforce, including lived experience peer workers.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".