Review of: "[Commentary] Balancing the bio in a biopsychosocial model of hazardous drinking and alcohol use disorders"
Bibliographic record
Abstract
Potential competing interests: No potential competing interests to declare.With this comment, the authors (Morris et al.) wish to criticize the very biologically and genetically focused posture taken by the authors of the article Hazardous Drinking and Alcohol Use Disorders (MacKillop et al., 2022) in their discussion of the etiology and treatment of problematic alcohol use and alcohol use disorders.The authors point out that MacKillop et al. place an excessive emphasis, with often little evidence support, on the genetic determinants of these behaviors, while more than justifiably ignoring the weight of environmental factors.If the commented article has a very individual focus, the commentary wishes to widen the perspectives by reintroducing the role of population-level factors, both in the understanding of problematic alcohol use behaviors and associated disorders, and in their treatment.Morris et al. offer arguments as to how a population-based approach would be more effective, pragmatic, and equitable than a purely individual approach as derived from an exclusive consideration of the weight of biogenetic factors.The authors also point out how an overemphasis on the individual, and implying for instance that only a minority are inherently "destined" to develop issues related to their alcohol use, can be problematic, and that this is a position that is often held by the alcohol industry and has the potential to influence policy decisions, with implications for the availability of services for those who may need them.Throughout the text, the authors highlight the importance of thinking carefully about the modeling adopted to reflect these phenomena, as this can lead directly and indirectly to creating and sustaining other issues, such as stigmatization and inability to provide tailored services for the needs of people who present a hazardous alcohol use or an AUD.In the best of all possible worlds, one should probably be able to consider both biomedical and psychosocial sets of factors, which may contribute together to these phenomena, at different levels, moments, effects, etc.This balanced model (i.e., closer to a true biopsychosocial model) is not that developed in the commentary, but I do understand the authors' perspective in favor of psychosocial factors, considering the traditional weight of biogenetic factors in the scientific literature, and our societies.Perhaps this would be a point to develop further?Finally, while this article focuses heavily on the issue of alcohol use and AUD, I think that these questions could be extended to all psychoactive substances, including illegal/illicit ones, where the weight of psychosocial factors is probably even more significant.The perspectives of the authors of this commentary are interesting, and probably should go beyond
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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 | 0.006 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.031 | 0.030 |
| Insufficient payload (model declined to judge) | 0.027 | 0.015 |
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".