{"title":"Naturalistic Research on Recovery Processes: Looking to the Future.","authors":"Robert L Stout","doi":"10.35946/arcr.v41.1.02","DOIUrl":null,"url":null,"abstract":"<p><p>Because recovery is an ongoing process, conducting research on the recovery process presents multiple challenges. The process can play out over many years, but change also can occur quickly. Although researchers are keenly interested in the precursors of these sudden changes, a researcher is unlikely to be present at critical moments; however, technology offers new options not available in prior years. Recovery research at this point, however, must be pursued largely through observational methods. Experiments involving aspects of recovery can and should be done, but observation is an essential part of recovery research. Hence, this paper focuses on technologies for conducting and analyzing observational studies. The author briefly reviews methods for gathering intensive longitudinal data and discusses how recovery researchers can take advantage of existing technology to delve more deeply into the complex processes associated with recovery and relapse. The future of recovery research, however, will require examining new ways of investigating recovery phenomena, including a new option for gathering data based on decision theory. Taking maximum advantage of existing and new technology for recovery research will require increasing collaboration between recovery researchers and quantitative scientists.</p>","PeriodicalId":7736,"journal":{"name":"Alcohol Research : Current Reviews","volume":"41 1","pages":"02"},"PeriodicalIF":6.8000,"publicationDate":"2021-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7846291/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Alcohol Research : Current Reviews","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.35946/arcr.v41.1.02","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"SUBSTANCE ABUSE","Score":null,"Total":0}
引用次数: 0
Abstract
Because recovery is an ongoing process, conducting research on the recovery process presents multiple challenges. The process can play out over many years, but change also can occur quickly. Although researchers are keenly interested in the precursors of these sudden changes, a researcher is unlikely to be present at critical moments; however, technology offers new options not available in prior years. Recovery research at this point, however, must be pursued largely through observational methods. Experiments involving aspects of recovery can and should be done, but observation is an essential part of recovery research. Hence, this paper focuses on technologies for conducting and analyzing observational studies. The author briefly reviews methods for gathering intensive longitudinal data and discusses how recovery researchers can take advantage of existing technology to delve more deeply into the complex processes associated with recovery and relapse. The future of recovery research, however, will require examining new ways of investigating recovery phenomena, including a new option for gathering data based on decision theory. Taking maximum advantage of existing and new technology for recovery research will require increasing collaboration between recovery researchers and quantitative scientists.
期刊介绍:
Alcohol Research: Current Reviews (ARCR) is an open-access, peer-reviewed journal published by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) at the National Institutes of Health. Starting from 2020, ARCR follows a continuous, rolling publication model, releasing one virtual issue per yearly volume. The journal offers free online access to its articles without subscription or pay-per-view fees. Readers can explore the content of the current volume, and past volumes are accessible in the journal's archive. ARCR's content, including previous titles, is indexed in PubMed, PsycINFO, Scopus, and Web of Science.