A Web-Based Alcohol Risk Communication Tool: Development and Pretesting Study
Bibliographic record
Abstract
BACKGROUND: Alcohol use is a major public health concern associated with an increased risk of morbidity and mortality. Health professionals in primary care commonly see patients with a range of alcohol-related risks and problems, providing an ideal opportunity for screening and brief intervention (BI). OBJECTIVE: This study aimed to develop a prototype for a Web-based tool for use by primary care health professionals (eg, doctors and nurses) to communicate alcohol harm risk to their patients and to engage with them regarding ways this risk could be reduced. METHODS: Following conceptualization and development of prototype wireframes, formative work and pretesting were undertaken. For the formative work, focus groups and key informant interviews were conducted with potential end users of the risk communication tool, including health professionals and consumers. The focus groups and interviews explored perceptions of alcohol risk communication and obtained feedback on the initial prototype. For pretesting, participants (primary care doctors and nurses) completed a Web-based survey followed by a period of pretesting before completion of a follow-up survey. The study was designed to gain feedback on the tool's performance in real-world settings as well as its relevance, ease of use, and any suggested refinements. RESULTS: In the formative work stage, 11 key informants and 7 consumers participated in either focus groups or individual interviews. Participants were very positive about the prototype and believed that it would be useful and acceptable in practice. Key informants identified that the key point of difference with the tool was that it provided all the pieces in 1 place (ie, assessment, interpretation, and resources to support change). Participants provided feedback on how the tool could be improved, and these suggestions were incorporated into the prototype where possible. In the pretesting stage, 7 people (5 doctors and 2 primary care nurses) completed the pretesting. Participants reported that the tool provided a useful framework for an intervention, that it would be acceptable to patients, that it was easy to use, that they would be likely to use it in practice, and that there were no technical issues. CONCLUSIONS: The alcohol risk communication tool was found to be acceptable and has the potential to increase the confidence of health professionals in assessing risk and providing BI.
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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 | 0.030 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".