Recent studies have reported genetic associations with alcohol-related cognitions, including alcohol expectancies, drinking refusal self-efficacy, drinking motives, and implicit measures of alcohol-related motivation [51,52, ]. Overall, the body of research on genetic influences on relapse and related processes is nascent and virtually all findings require replication. Consistent with the broader literature, it can be anticipated that most genetic associations with relapse outcomes will be small in magnitude and potentially difficult to replicate.
Withdrawal tendencies can develop early in the course of addiction [25] and symptom profiles can vary based on stable intra-individual factors [63], suggesting the involvement of tonic processes. Despite serving as a chief diagnostic criterion, withdrawal often does not predict relapse, perhaps partly explaining its de-emphasis in contemporary motivational models of addiction [64]. However, recent studies show that withdrawal profiles are complex, multi-faceted and idiosyncratic, and that in the context of fine-grained analyses withdrawal indeed can predict relapse [64,65]. Such findings have contributed to renewed interest in negative reinforcement models of drug use [63].
Many therapies (both behavioral and pharmacological) have been developed to help individuals cease or reduce addictive behaviors and it is critical to refine strategies for helping individuals maintain treatment goals. As noted by McLellan [138] and others [124], it is imperative that policy makers support adoption of treatments that incorporate a continuing care approach, such that addictions treatment is considered from a chronic (rather than acute) care perspective. Broad https://ecosoberhouse.com/ implementation of a continuing care approach will require policy change at numerous levels, including the adoption of long-term patient-based and provider-based strategies and contingencies to optimize and sustain treatment outcomes [139,140]. Specific intervention strategies include helping the person identify and cope with high-risk situations, eliminating myths regarding a drug’s effects, managing lapses, and addressing misperceptions about the relapse process.
Despite the intense controversy, the Sobell’s high-profile research paved the way for additional studies of nonabstinence treatment for AUD in the 1980s and later (Blume, 2012; Sobell & Sobell, 1995). Marlatt, in particular, became well known for developing nonabstinence treatments, such as BASICS for college drinking (Marlatt et al., abstinence violation effect 1998) and Relapse Prevention (Marlatt & Gordon, 1985). Like the Sobells, Marlatt showed that reductions in drinking and harm were achievable in nonabstinence treatments (Marlatt & Witkiewitz, 2002). The AVE was introduced into the substance abuse literature within the context of the “relapse process” (Marlatt and Gordon 1985, p. 37).
About 10% of individuals who report cannabis use in the past year meet criteria for a cannabis use disorder, while this proportion increases to 18%, 19%, 58%, and 65% of those with past year use of cocaine, opioids (misuse), methamphetamine, and heroin, respectively. These data suggest that non-disordered drug use is possible, even for a substantial portion of individuals who use drugs such as heroin (about 45%). However, they do not elucidate patterns of non-disordered use over time, nor the likelihood of maintaining drug use without developing a DUD. It is essential to understand what individuals with SUD are rejecting when they say they do not need treatment. In this model, treatment success is defined as achieving and sustaining total abstinence from alcohol and drugs, and readiness for treatment is conflated with commitment to abstinence (e.g., Harrell, Trenz, Scherer, Martins, & Latimer, 2013). Additionally, the system is punitive to those who do not achieve abstinence, as exemplified by the widespread practice of involuntary treatment discharge for those who return to use (White, Scott, Dennis, & Boyle, 2005).
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