Development of an Electronic Alcohol Screening and Brief Intervention Program for Hospital Outpatients With Unhealthy Alcohol Use
Bibliographic record
Abstract
BACKGROUND: Alcohol screening and brief intervention is recommended for widespread implementation in health care systems, but it is not used routinely in most countries for a variety of reasons. Electronic screening and brief intervention (e-SBI), in which patients complete a Web-based questionnaire and are provided with personalized feedback on their drinking, is a promising alternative to practitioner delivered intervention, but its efficacy in the hospital outpatient setting has not been established. OBJECTIVE: The objective of our study was to establish the feasibility of conducting a full-scale randomized controlled trial to determine whether e-SBI reduces alcohol consumption in hospital outpatients with hazardous or harmful drinking. METHODS: The study was conducted in the outpatient department of a large public hospital in Newcastle (population 540,000), Australia. Adults with appointments at a broad range of medical and surgical outpatient clinics were invited to complete an e-SBI program on a laptop, and to report their impressions via a short questionnaire. Follow-up assessments were conducted 2-8 weeks later by email and post. RESULTS: We approached 172 outpatients and 108/172 (62.8%) agreed to participate. Of the 106 patients capable of self-administering the e-SBI, 7/106 (6.6%) did not complete it (3 due to technical problems and 4 because they were called for their appointment), 15/106 (14.2%) indicated that they had not consumed any alcohol in the past 12 months, 43/106 (40.6%) screened negative for unhealthy alcohol use (scored less than 5 on the Alcohol Use Disorders Identification Test Consumption [AUDIT-C] questions), 33/106 (31.1%) screened positive for hazardous or harmful drinking (AUDIT-C score 5-9), and 8/106 (7.5%) screened positive for possible alcohol dependence (AUDIT-C score 10-12). Among the subgroup with hazardous or harmful drinking, 27/33 (82%) found the feedback on their drinking very, quite, or somewhat useful, 33/33 (100%) thought the intervention would appeal to most or some of the people who attend the service, and 22/30 (73%) completed the follow-up. We also found that some well established procedures used in trials of e-SBI in the primary care setting did not translate to the hospital outpatient setting (1) we experienced delays because the e-SBI program had to be developed and maintained by the health service's information technology staff for security reasons, (2) recruiting patients as they left the reception desk was impractical because patients tended to arrive at the beginning of the clinics with few arrivals thereafter, and (3) use of a laptop in a fixed location resulted in some patients rushing through the e-SBI so they could return to their seat in the area they had been advised to wait in. CONCLUSIONS: e-SBI is acceptable to outpatients and with some adaptation to organizational and physical conditions, it is feasible to recruit and screen patients and to deliver the intervention without disrupting normal service provision. This suggests that e-SBI could be provided routinely in this important setting if shown to be efficacious.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".