Implementing Screening, Brief Intervention and Referral Intervention for Health Promotion and Disease Prevention in Hospital Settings in Alberta: A Pilot Study
Bibliographic record
Abstract
Objective: This study assessed the feasibility of implementing screening, brief intervention and referral (SBIR) intervention in hospital settings. Methods: This cross-sectional study evaluated the implementation of the SBIR intervention in a hospital in Alberta for tobacco use, alcohol intake, physical inactivity, and insufficient vegetable and fruit consumption. Patients were interviewed approximately 4-month later to collect data on the acceptability and effectiveness of the intervention received (n = 108). The data were primarily analyzed using descriptive statistics. Results: Of 108 patients, >80% agreed that “they were ok with being screened” for the risk factors during their hospital visit. Up to 68% of patients recalled the provider’s brief education. At the follow-up, 20% of patients quit tobacco, 50% reduced alcohol use, 30% increased physical activity, and 25% increased vegetable and fruit intake. Conclusion: Risk factor screening was acceptable for patients. Patients recalled the brief education they received from healthcare providers. Patients reported risk-reducing changes in their risk factors. Our future work will integrate the SBIR approach within the Electronic Clinical Information System and use robust research methods to investigate the impact of SBIR on patients’ behavior change.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".