SBIRT (Screening, Brief Intervention, and Referral to Treatment) Among Trauma Patients: A Review of the Inpatient Process and Patient Experience
Bibliographic record
Abstract
Screening, brief intervention, and referral to treatment (SBIRT) is an important and effective strategy among injury prevention measures aimed at reducing risky alcohol use (). The trauma patient population is at significant risk for alcohol-related trauma recidivism () and is therefore a priority group in which to implement SBIRT. Vancouver General Hospital (VGH) implemented SBIRT on its 2 inpatient trauma units in the fall of 2014. The alcohol use disorders screening test (AUDIT-C) was chosen as the screening tool for nurses to complete with new patients. A brief intervention was conducted by the trauma social workers in the cases where a patient scored positive on the AUDIT-C. To evaluate the implementation and effectiveness of SBIRT on the 2 inpatient trauma units at VGH and to provide recommendations for improvement, a telephone survey of past trauma patients and a review of the screening process were undertaken in May 2016. Patient follow-up was conducted via a telephone survey. Of the 79 patients who met the follow-up criteria, a total of 19 were successfully contacted. Results from the survey showed that the majority of patients did not recall being screened with the AUDIT-C and were either unsure or did not recall receiving a brief intervention by the social worker. Despite these findings, a rescreening with the AUDIT-C tool revealed that 68% of patients who participated in the survey had a lower score than when they were inpatients. Recommendations for improvement include optimizing the timing of SBIRT with trauma inpatients and implementing a follow-up system. The literature suggests that following up with patients to provide an SBIRT "booster" increases the effectiveness of brief interventions (C. ).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".