Clinicians’ perspectives and perceived barriers to caring for patients with alcohol use disorder and cirrhosis
Bibliographic record
Abstract
BACKGROUND: Alcohol use disorder (AUD) is one of the leading etiologies for liver cirrhosis and liver transplantation. Few individuals with AUD receive guideline-based care in the form of screening, brief intervention, referral to treatment, or prescription of pharmacotherapy for relapse prevention. We interviewed clinicians across Alberta to assess the current experience and perceived barriers to managing AUD in people who have cirrhosis. The aim of this paper is to summarize these findings to inform the development of an educational intervention. METHODS: We used a qualitative descriptive approach to explore the experiences of clinicians who care for patients with cirrhosis and AUD in Alberta. We conducted semi-structured interviews directed by an interview guide. Interviews were recorded and transcribed verbatim. We used an inductive thematic analysis approach whereby transcripts were coded, with codes grouped into larger categories, then themes. RESULTS: Sixteen clinicians participated in this study. Many participants acknowledged that they do not use a standardized approach to screening, brief intervention, and referral to treatment. Through thematic analysis we identified four themes surrounding barriers to managing AUD in patients with cirrhosis: (i) Practicing within knowledge constraints, (ii) Navigating limited resources and system challenges, (iii) Balancing the complexity of cirrhosis and AUD, and (iv) Acknowledging the influence of provider perceptions on care. CONCLUSION: This article presents the perspectives of clinicians who care for people who have AUD and cirrhosis. Significant barriers exist, including limited knowledge and resources, systemic challenges, and patient complexity. The information gathered will be used to develop an educational intervention that will delve deeper into these issues in order to have the greatest impact on clinicians who routinely interface with this patient population.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".