Tailored interventions to support the implementation of the German national guideline on screening, diagnosis and treatment of alcohol-related disorders: a project protocol
Bibliographic record
Abstract
Abstract. Background: The German Guideline on Screening, Diagnosis and Treatment of Alcohol Use Disorders aims to increase the uptake of evidence-based interventions for the early identification, diagnosis, prevention and treatment of alcohol-related disorders in relevant healthcare settings. To date, dissemination has not been accompanied by a guideline implementation strategy. The aim of this study is to develop tailored guideline implementation strategies and to field-test these in relevant medical and psycho-social settings in the city of Bremen, Germany. Methods: The study will conduct an impact and needs assessment of healthcare provision for alcohol use orders in Bremen, drawing on a range of secondary and primary data to: evaluate existing healthcare services; model the potential impact of improved care on public health outcomes; and identify potential barriers and facilitators to implementing evidence-based guidelines. Community advisory boards will be established for the selection of single-component or multi-faceted guideline implementation strategies. The tailoring approach considers guideline, provider and organizational factors shaping implementation. In field tests quality outcome indicators of the delivery of evidence-based interventions will be evaluated accompanied by a process evaluation to examine patient, provider and organizational factors. Outlook: This project will support the translation of guideline recommendations for the identification, prevention and treatment of AUD in routine practice and therefore contributes to the reduction of alcohol-related burden in Germany. The project is running since October 2017 and will provide its main outcomes by end of 2020. Project results will be published in scientific journals and presented at national and international conferences.
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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 | 0.055 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.011 |
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".