A realist review of brief interventions for alcohol misuse delivered in emergency departments
Bibliographic record
Abstract
BACKGROUND: Brief interventions (BIs) involve screening for alcohol misuse and providing feedback to patients about their use, with the aim of reducing alcohol consumption and related consequences. BIs have been implemented in various healthcare settings, including emergency departments (ED), where they have been found to contribute mixed results in their ability to address alcohol misuse among adults. Mechanisms through which BIs work and contextual factors impacting BI effectiveness are not clear. The purpose of this review was to understand how, for whom, and under what circumstances BIs work for adults misusing alcohol and who have been admitted to an ED. A realist review was chosen to answer these questions as realist reviews create context-mechanism-outcome configurations, leading to the development of comprehensive and detailed theories; in this case explaining how and for whom BIs work. METHODS: Databases including PsycINFO, Healthstar, CINAHL, Medline, and Nursing and Allied Health were searched for articles published until December 2013. The search strategy focused on studies examining BIs that targeted alcohol misuse among adults admitted into the ED. The search identified 145 relevant abstracts, of which 36 were included in the review. The literature was synthesized qualitatively (immersion/crystallization). RESULTS: Four mechanisms were found within reviewed studies, including engagement in/retention of BI materials, resolving ambivalence, increased awareness/insight into consequences of drinking, and increased self-efficacy/empowerment to use skills for change. The following contexts were found to impact mechanisms: emotional state, injury attributed to alcohol use, severity of alcohol use, and baseline stage of change. CONCLUSIONS: This realist review provides advances in theories regarding which mechanisms to target during a BI and which contexts create the most favorable conditions for these mechanisms to occur, ultimately leading to optimal BI outcomes. These results can inform future clinical decision-making when delivering BIs in ED settings. Future research should conduct quantitative examination to confirm these findings. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42013006549.
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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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".