Self-management with alcohol over lifespan: psychological mechanisms, neurobiological underpinnings, and risk assessment
Bibliographic record
Abstract
Self-management includes all behavioural measures and cognitive activities aimed at coping with challenges arising throughout the lifespan. While virtually all of these challenges can be met without pharmacological means, alcohol consumption has long been instrumentalized as a supporting tool to help coping with problems arising selectively at adolescence, adulthood, and ageing. Here, we present, to our knowledge, the first systematic review of alcohol instrumentalization throughout lifespan. We searched MEDLINE, Google Scholar, PsycINFO and CINAHL (from Jan, 1990, to Dec, 2022) and analysed consumption patterns, goals and potential neurobiological mechanisms. Evidence shows a regular non-addictive use of alcohol to self-manage developmental issues during adolescence, adulthood, and ageing. Alcohol is selectively used to overcome problems arising from dysfunctional personality traits, which manifest in adolescence. A large range of psychiatric disorders gives rise to alcohol use for the self-management of distinct symptoms starting mainly in adulthood. We identify those neuropharmacological effects of alcohol that selectively serve self-management under specific conditions. Finally, we discuss the adverse effects and associated risks that arise from the use of alcohol for self-management. Even well-controlled alcohol use adversely impacts health. Based on these findings, we suggest the implementation of an entirely new view. Health policy action may actively embrace both sides of the phenomenon through a personalized informed use that allows for harm-controlled self-management with alcohol.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".