Usability Testing a Context-Sensitive Strategy for Screening, Brief Intervention, and Referral to Treatment
Bibliographic record
Abstract
Screening, Brief Intervention, and Referral to Treatment (SBIRT) is an evidence-based process healthcare personnel use to screen, manage, and triage patients struggling with substance use. The process requires clinic staff to furnish patients with structured screening questions. Providers can then offer treatment and mental health referral when indicated. Our team recently deployed a digital tablet-based version of the SBIRT screening questions in primary care. However, we needed to assess patient-reported usability of our approach because negative perceptions could limit clinic adoption, patient completion of the process, and effective referral. We, therefore, conducted a usability evaluation of our digital SBIRT screening instrument using a cross-sectional patient survey. Most participants (64.2%) reported completing the screening questions in under five minutes, with no reports of completion times exceeding fifteen minutes. Our results suggest the tablet-based SBIRT screener is easy to understand and can be efficiently completed before a clinical encounter. Furthermore, patients believed the digital SBIRT screener increases clinician awareness of patient health issues and promotes positive action. These findings support the continued use, wider adoption, and integration of digital SBIRT tools in clinical settings.
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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 | 0.039 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".