Bibliographic record
Abstract
Alcohol consumption, depression, and anxiety are conditions that have significant implications for individual well-being and public health both independently and together. Alcohol use disorders frequently co-occur with depression and anxiety disorders, with each condition exacerbating the severity and chronicity of the others. The bidirectional nature of these relationships suggests a cyclical pattern wherein alcohol misuse may serve as a maladaptive coping mechanism for individuals experiencing symptoms of depression and anxiety, while excessive alcohol consumption can precipitate or worsen these mental health conditions over time. Neurobiological mechanisms underpinning these associations include neurotransmitter system disruptions, alterations in stress response pathways, and structural changes in brain regions implicated in emotional regulation and reward processing. Moreover, psychosocial factors such as adverse childhood experiences, social isolation, and socioeconomic stressors contribute to the development and perpetuation of this comorbidity. Effective interventions for addressing these conditions include a multidimensional approach with pharmacotherapy, psychotherapy, and psychosocial support in the context of a chronic care model. Integrated treatment approach that simultaneously target substance use and mental health symptoms when there is a co-occurrence have demonstrated superior efficacy compared to standalone interventions. Preventive strategies should focus on early identification of individuals at heightened risk for developing alcohol-related mental health problems, as well as implementing policies aimed at reducing alcohol availability, promoting mental health literacy, and enhancing access to evidence-based treatment services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.013 |
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".