An Interprofessional Education Pilot Program on Screening, Brief Intervention, and Referral to Treatment (SBIRT) Improves Student Knowledge, Skills, and Attitudes
Bibliographic record
Abstract
BackgroundRecent efforts to prepare healthcare professionals to care for patients/clients with substance use problems have incorporated SBIRT (Screening, Brief Intervention, and Referral to Treatment) into graduate education programs. No research has examined the benefits and methods of an SBIRT interprofessional education approach for behavioral health graduate students and medical residents. This pilot study examined the implementation of an interprofessional curriculum on SBIRT to improve attitudes, abilities, skills, and knowledge of learners planned by faculty from multiple professions at a state university.MethodsFaculty in Counseling, Family Medicine, Internal Medicine, Nursing and Social Work departments collaborated to develop an interprofessional curriculum delivered through a small-group and active learning approach. Seventy-one residents and graduate students participated. Pre- and post-training surveys measured self-perceived attitudes, abilities, and skills along with objectively measured knowledge. Analysis examined pre- to post-training changes in scores.ResultsPre-training surveys yielded an 89% response rate; post-training, 85%. Self-perceived attitudes did not change significantly, except a 20% increase in how rewarded learners felt while working with patients/clients with alcohol/drug use disorders (P < .01). Compared to baseline, there was a statistically significant increase in all items of self-perceived ability (P<.01) and all items of self-perceived communication skills (P<=.04). Knowledge mean scores also increased significantly (P < .001) across both primary care and behavioral health learner groups.ConclusionsInterprofessional training in SBIRT produced improvements in ability, skills, knowledge, and some attitudes. Such programs may inform providers who care for patients/clients with substance use problems, thus improving their competence and personal experience.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".