SCREENING, BRIEF INTERVENTION, AND REFERRAL TO TREATMENT (SBIRT) FOR ADOLESCENT ALCOHOL USE IN EMERGENCY DEPARTMENTS: A NATIONAL SURVEY OF PEDIATRIC EMERGENCY PHYSICIANS
Bibliographic record
Abstract
BACKGROUND: Problematic alcohol use is hazardous to adolescents – early use is a predictor of later dependence or abuse – and is associated with harmful health consequences. Screening, brief intervention, and referral to treatment (SBIRT), the recommended clinical approach for adolescent alcohol-related concerns, is underused in emergency departments (ED). Use of electronic tools to conduct SBIRT (technology-based SBIRT) in the ED has been shown to reduce subsequent alcohol-related consequences, and is currently regarded as a promising strategy to engage clinicians, and facilitate standardized and efficient care. A better understanding of ED physicians’ decision-making around SBIRT practice and technology acceptance is needed. OBJECTIVES: To examine pediatric emergency physicians’ alcohol SBIRT practices, perceptions of adolescent drinking and treatment, and acceptance of technology-based SBIRT. DESIGN/METHODS: We conducted a cross-sectional, pan-Canadian survey using a validated questionnaire based on the Substance Abuse and Attitude Scale and the Technology Acceptance Model. We surveyed pediatric emergency physicians sourced from the Pediatric Emergency Research Canada database (n=245). The questionnaire was administered through a web-based platform and paper-based mail-outs. Recruitment followed a modified Dillman four-contact approach and spanned October 2016 to January 2017. RESULTS: A total of 164 of 245 physicians (67%) responded. Twenty-five percent of respondents reported never having practiced SBIRT; 1.2% consistently administered all, or part of, SBIRT when treating adolescents for alcohol-related visits. A lack of appropriate tools (49%) and time constraints (41%) were the main reasons for SBIRT underutilization, as reported by physicians. Most physicians (85%) felt responsible for addressing alcohol problems with adolescent patients, however, confidence in knowledge and abilities for SBIRT execution was reported as low. Responses were moderately positive for the perceived value, usability, and potential impact of technology-based SBIRT. Physicians across all frequency levels of current SBIRT use indicated that an electronic tool would increase SBIRT practices. CONCLUSION: The routine use of SBIRT by pediatric emergency clinicians is lacking. However, we identified common barriers to use – resources and training – that can be feasibly addressed. Technology-based SBIRT is an acceptable innovation that physicians are willing to consider for use during the care of adolescents. Strategies to support implementation in the ED are necessary.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".