Multidisciplinary Perspectives on a New Hospital Addiction Consult Service: A Mixed- Methods Study
Bibliographic record
Abstract
Background: Hospital-based Addiction Consult Services (ACS) are increasingly implemented to improve care for patients with substance use disorders (SUD). While ACS are generally well-regarded, clinicians of various roles may hold different perceptions of their impact. Methods: We conducted a web-based survey of physicians, advanced practice providers (APP), and nurses at a Philadelphia academic hospital from August-September 2024, approximately 18 months years after ACS implementation. The survey assessed attitudes and perceptions of the ACS with 6 questions using a 5-point Likert scale. These data were integrated with results from semi-structured interviews with physicians, APP, and nurses from November 2023-January 2024, 7-9 months after ACS implementation, to provide a richer understanding of provider perspectives on the impact of the ACS. We used descriptive statistics to characterize the samples and analyzed survey data by clinician group chi-squared tests. Interviews were analyzed using thematic content analysis. Results: Of 793 clinicians surveyed, 311 responded (39%), including 128 nurses (41%), 108 resident physicians (35%), 49 attending physicians (16%), and 26 APPs (8%). Most providers reported that the ACS positively impacted patient care. Surveyed nurses reported significantly smaller perceived improvements in quality of care and communication compared to other clinicians (43-63% nurses vs. 77-98% other clinicians, p < 0.001, for 5 of 6 questions). While qualitative feedback was positive across groups, nursing interview narratives emphasized communication gaps, limited integration between nursing and the ACS, and a desire for additional training and education around SUD care. Conclusions: While inpatient ACS improve key aspects of SUD care across professional groups, their impact may be enhanced through intentional integration of all frontline providers. Embedding nurses or nurse educators into ACS structures, strengthening multidisciplinary collaboration, and providing standardized SUD training may enhance team cohesion and ensure that all providers feel equipped and supported in caring for patients with SUD.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".