Cognitive impairment among alcohol treatment service users in South Wales: an exploratory examination of typologies of behaviour, impairment, and service attendance
Bibliographic record
Abstract
Introduction: Alcohol dependence is a global issue with many negative consequences, including alcohol-related brain damage (ARBD). Assessment of the sociodemographic and cognitive characteristics of individuals with confirmed or suspected ARBD presenting to alcohol services warrants further investigation. Methods: This study retrospectively examined rates of cognitive impairment using Montreal Cognitive Assessment (MoCA) data from 300 adults who visited three alcohol support services. We demonstrate that 55.3% of the sample had significant levels of cognitive impairment. Females' cognitive performance was disproportionately negatively affected by historical alcohol use relative to males. Results: The analysis identified four categories of participants, and the majority had a long history (+10 years) of alcohol use and were still actively drinking. Those taking part in active treatment for ARBD or practising abstinence demonstrated lower levels of cognitive impairment. Additionally, prior access to specialised ARBD care was associated with higher MoCA scores. Discussion: This research has identified a range of key service engagement, sociodemographic and cognitive characteristics that could be used to optimise support for those with alcohol dependence, whilst also highlighting some critical questions to be addressed in future research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".