Content comparison of guideline-recommended instruments used in treatment for alcohol use disorders
Bibliographic record
Abstract
PURPOSE: Practice guidelines recommend the use of standardized instruments in the treatment of alcohol use disorders (AUDs); however, the extent to which these instruments assess patients' functioning is unclear. The aim of this study was to examine the domains of functioning and contextual factors contained in guideline-recommended instruments, using the International Classification of Functioning, Disability, and Health (ICF) as a reference. MATERIALS AND METHODS: We identified instruments by reviewing AUD treatment guidelines used in Germany, Canada, Australia and New Zealand, United Kingdom, and United States. We included instruments which were available in English free of charge, we excluded instruments developed solely for diagnostic or epidemiological purposes and those for children or adolescents. Following a standardized set of rules, two health care researchers identified the concepts contained in the items on the instruments and independently linked them to ICF categories. RESULTS: A total of 10 instruments were included. Among 517 items, 752 meaningful concepts (MCs) were derived, and 622 of them were linked to the ICF. Inter-rater agreement was κ = 0.61. One hundred eighty eight MCs referred to personal factors, 175 to body functions, 168 to activity and participation, and 91 to environmental factors. The most frequently linked ICF chapter was b1 (mental functions). CONCLUSIONS: Instruments recommended in AUD treatment guidelines vary considerably in their assessment of patients' functioning and contextual factors. Within the investigated instruments, environmental factors are under-represented in comparison to body functions and personal factors. ICF linkage provides guidance for clinicians and researchers in the selection of appropriate instruments. Implications for rehabilitation Since instruments that are recommended in alcohol treatment guidelines vary considerably in respect the functioning domains and context factors they cover, it may be challenging for clinicians to select instruments relevant to their treatment context. Using the ICF as framework, our results provide guidance for clinicians in how to select appropriate instruments. Within the investigated instruments, environmental factors and activities and participation are under-represented in comparison to body functions and personal factors. Clinicians may employ AUD-unspecific or ICF-based instruments to cover these components if needed.
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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 | 0.034 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".