Alcohol Abuse Prevention and Treatment in Health Care Settings: Screening and Brief Intervention
Bibliographic record
Abstract
INTRODUCTIONSubstance misuse in the United States represents is a large and far reaching problem. Alcohol and drug misuse patterns and related behaviors have an impact across socioeconomic status, age, ethnicity, and other demographic categories. For instance, approximately 10 percent of men and women in the United States are dependent on alcohol (NIAAA, 2006). This prevalence brings with it serious impacts on society and individuals as the annual costs for alcohol related services is over $192 billion (Kirschstein, 2000). These costs stem from, services related to alcohol treatment, medical affects, lost earnings and criminal justice issues (Miller and Hendrie, 2009).Costs to society for alcohol use manifests through the actions of individuals who use. For instance, from a nationally representative survey of crime victims and those who experienced problems caused by those under the influence of alcohol (Greenfield, et al., 2009), respondents indicated they had been assaulted by someone who had been drinking (28%), had experienced familial or marital problems because of another's drinking (18%), had their property vandalized by someone who had been drinking (12%), experienced a motor vehicle accident because of someone's drinking (8%), and/or had undergone financial trouble stemming from another's drinking (7%) (Greenfield, et al., 2009). In connection with these issues, researchers from the Bureau of Justice Statistics reported that nearly a quarter of jail inmates reported to have a history of alcohol dependence, and a quarter reported to have a history of alcohol abuse (Karberg and James, 2005).Beyond societal costs of substance use, individuals also can pay a high price in their personal health. Mild to severe brain damage has been observed in about half of alcoholic drinkers (Berman and Marinkovic, 2003). Muscle fiber degradation has also been noted as a result of sustained patterns of individual alcohol misuse (Preedy, et al., 2005); 10-15 percent of individuals suffering from alcoholism eventually develop cirrhosis (Mann, Smart, and Govoni, 2003). Prolonged alcohol use can also cause in a lack of vitamin B1 in the human brain, known as Wernicke-Korsakoff syndrome. Common symptoms of this syndrome are the loss of muscle coordination, vision impairments, confusion of thoughts, and memory dysfunction (Medline Plus, 2010). Furthermore, in severe cases of sustained alcohol use, withdrawal symptoms can escalate into delirium tremens, a severe form of withdrawal accompanied by symptoms such as confusion, seizures, and/or altered mental status.The consequences of alcohol use require actions to assist individuals in reducing or abstaining from future use. Although some individuals may be able to curb substance use through their own efforts, others seek out community resources, such as 12-Step programs or recovery centers to receive professional assistance to reduce or eliminate use patterns. Nevertheless, it is not a common occurrence for persons with alcohol problems to seek specialized treatment for their alcohol use (NIH, 1995); therefore, other settings for treatment must be identified and used to assist these individuals. One setting in particular that has been identified as a location where individuals with substance issues can be potentially identified and provided with treatment or referral to treatment is medical care settings. These include: 1) primary medical care settings, 2) hospital emergency department (ED) settings and 3) hospital trauma department settings.Despite the need to identify, treat, or refer patients with alcohol issues in these settings, provider contact can be somewhat constricted due to provider work demands (Babor, Ritson, and Hodgson, 1986). Therefore, alcohol abuse services that have been created for delivery in these settings have been designed to be brief. Brief screening instruments (Mayfield, McLeod, and Hall, 1974; Selzer, 1971) and interventions were formulated and first began to be tested approximately 30 years ago (Chick, Lloyd, and Crombie, 1985; Kristenson, Ohlin, Hulten-Nosslin, Trell, and Hood, 1983). …
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".