Reducing and preventing alcohol misuse and its consequences: A Grand Challenge for social work
Bibliographic record
Abstract
Begun, A., Clapp, J., & The Alcohol Misuse Grand Challenge Collective (2016). Reducing and preventing alcohol misuse and its consequences: A Grand Challenge for social work. The International Journal Of Alcohol And Drug Research, 5(2), 73-83. doi:http://dx.doi.org/10.7895/ijadr.v5i2.223In both the United States and throughout the world, alcohol misuse is associated with high rates of morbidity, mortality, and cooccurring physical and mental health problems. It causes an array of acute and chronic problems and contributes to extensive costs in every sector of society (e.g., health, mental health, education, legal, economic productivity). The scientific discovery, development, and implementation of evidence-informed solutions for alleviating alcohol-related problems are inherently multisectoral, as they affect individuals, families, communities, and larger social systems. The advent of new technologies, research approaches, and intervention strategies has dramatically accelerated positive results in addressing such problems over the past 40 years. Still, alcohol misuse remains a significant global problem, and reducing and preventing its consequences is a Grand Challenge for Social Work. This paper addresses the following points about the challenge: (1) it is large, important, and compelling; (2) it can be analyzed and assessed; (3) demonstrable progress can be made in a decade; (4) multisectoral collaboration is required to meet this challenge; and (5) sustainable solutions to the challenge require significant, transformative, and groundbreaking innovations.
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 | 0.058 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.019 | 0.027 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.034 | 0.044 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".