Transient Elastography and Video Recovery Narrative Access to Support Recovery From Alcohol Misuse: Development of a Novel Intervention for Use in Community Alcohol Treatment Services
Bibliographic record
Abstract
BACKGROUND: Mortality from alcohol-related liver disease has risen significantly for 3 decades. Transient elastography (TE) is a noninvasive test providing a numerical marker of liver disease. Preliminary evidence suggests that TE can reduce alcohol consumption. The KLIFAD (does knowledge of liver fibrosis affect high-risk drinking behavior?) study has developed a complex intervention wherein people receiving alcohol treatment are provided with access to TE, accompanied by scripted feedback tailored to their disease state, and access to video narratives describing alcohol misuse recovery after receiving TE. Recovery narratives are included due to preliminary evidence from mental health studies which suggest that access to digital narratives describing recovery from mental health problems can help people affected by mental health problems, including through mechanisms with the potential to be transferable to an alcohol treatment setting, for example, by increasing hope for the future, enabling learning from the experience of others, or promoting help-seeking behaviors. OBJECTIVE: We aimed to develop the KLIFAD intervention to the point that it could be delivered in a feasibility trial and to produce knowledge relevant to clinicians and researchers developing interventions making use of biomarkers of disease. METHODS: In research activity 1, standardized scripted feedback was developed by this study, and then iterated through focus groups with people who had experienced alcohol misuse and TE, and key alcohol workers with experience in delivering TE. We report critical design considerations identified through focus groups, in the form of sensitizing concepts. In research activity 2, a video production guide was coproduced to help produce impactful video-based recovery narratives, and a patient and public involvement (PPI) panel was consulted for recommendations on how best to integrate recovery narratives into an alcohol treatment setting. We report PPI recommendations and an overview of video form and content. RESULTS: Through research activity 1, we learnt that patient feedback has not been standardized in prior use of TE, that receiving a numeric marker can provide an objective target that motivates and rewards recovery, and that key alcohol workers regularly tailor information to their clients. Through research activity 2, we developed a video production guide asking narrators what recovery means to them, what helped their recovery, and what they have learned about recovery. We produced 10 recovery narratives and collected PPI recommendations on maximizing impact and safety. These led to the production of unplanned videos presenting caregiver and clinician perspectives, and a choice to limit narrative availability to alcohol treatment settings, where support is available around distressing content. These choices have been evaluated through a feasibility randomized controlled trial [ISRCTN16922410]. CONCLUSIONS: Providing an objective target that motivates and rewards recovery is a candidate change mechanism for complex interventions integrating biomarkers of disease. Recovery narratives can contain distressing content; intervention developers should attend to safe usage. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-054954.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".