{"title":"康复之家的潜在轮廓分析:一个单一的定量维度捕获了大部分但不是全部康复过程的重要细节。","authors":"Leonard A Jason, Mike Stoolmiller, John Light","doi":"10.1080/08897077.2021.1986880","DOIUrl":null,"url":null,"abstract":"<p><p><i>Background:</i> Our study explored whether latent classes adequately represented the social capital recovery indicators at the resident level and whether latent class membership predicted subsequent exits from the recovery homes. <i>Method</i>: Our sample included about 600 residents in 42 recovery homes. Over a 2-year period of time, every 4 months, data were collected on eight elements of recovery capital. <i>Results</i>: We found 5 latent classes were optimal for representing 8 elements of recovery capital. Representing 79% of the sample, 3 of the 5 latent class profiles of the means of the 8 recovery indicators were roughly parallel and differed only in level, but the remaining 2 latent class profiles, representing 21% of the sample, were not parallel to the first 3, suggesting that a single quantitative dimension of perceived recovery may capture most but not all of the important details of the recovery process. Next, using longitudinal data from homes, the distal outcomes of resident eviction and voluntary exit were found to be related to latent class membership. Resident level pre-existing predictors (e.g., employment status, educational attainment, gender, Latinx ethnicity) and house level pre-existing predictors (e.g., financial health, poverty level of typical population served, new resident acceptance rate) significantly discriminated the classes. In a model that combined both pre-existing predictors and distal outcomes, latent class membership was still the strongest predictor of evictions controlling for the pre-existing predictors. <i>Conclusions</i>: These classes help to clarify the different aspects of the recovery latent score, and point to classes that have different ethnic and gender characteristics as well as outcomes in the recovery homes. For example, the high levels of self-confidence found in class 3 suggest that Latinx might be at higher risk for having some difficulties within these recovery communities.</p>","PeriodicalId":22108,"journal":{"name":"Substance abuse","volume":"43 1","pages":"666-674"},"PeriodicalIF":2.8000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9153853/pdf/nihms-1810920.pdf","citationCount":"2","resultStr":"{\"title\":\"Latent profile analysis in recovery homes: A single quantitative dimension captures most but not all of the important details of the recovery process.\",\"authors\":\"Leonard A Jason, Mike Stoolmiller, John Light\",\"doi\":\"10.1080/08897077.2021.1986880\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><i>Background:</i> Our study explored whether latent classes adequately represented the social capital recovery indicators at the resident level and whether latent class membership predicted subsequent exits from the recovery homes. <i>Method</i>: Our sample included about 600 residents in 42 recovery homes. Over a 2-year period of time, every 4 months, data were collected on eight elements of recovery capital. <i>Results</i>: We found 5 latent classes were optimal for representing 8 elements of recovery capital. Representing 79% of the sample, 3 of the 5 latent class profiles of the means of the 8 recovery indicators were roughly parallel and differed only in level, but the remaining 2 latent class profiles, representing 21% of the sample, were not parallel to the first 3, suggesting that a single quantitative dimension of perceived recovery may capture most but not all of the important details of the recovery process. Next, using longitudinal data from homes, the distal outcomes of resident eviction and voluntary exit were found to be related to latent class membership. Resident level pre-existing predictors (e.g., employment status, educational attainment, gender, Latinx ethnicity) and house level pre-existing predictors (e.g., financial health, poverty level of typical population served, new resident acceptance rate) significantly discriminated the classes. In a model that combined both pre-existing predictors and distal outcomes, latent class membership was still the strongest predictor of evictions controlling for the pre-existing predictors. <i>Conclusions</i>: These classes help to clarify the different aspects of the recovery latent score, and point to classes that have different ethnic and gender characteristics as well as outcomes in the recovery homes. For example, the high levels of self-confidence found in class 3 suggest that Latinx might be at higher risk for having some difficulties within these recovery communities.</p>\",\"PeriodicalId\":22108,\"journal\":{\"name\":\"Substance abuse\",\"volume\":\"43 1\",\"pages\":\"666-674\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9153853/pdf/nihms-1810920.pdf\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Substance abuse\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/08897077.2021.1986880\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"SUBSTANCE ABUSE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Substance abuse","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/08897077.2021.1986880","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"SUBSTANCE ABUSE","Score":null,"Total":0}
Latent profile analysis in recovery homes: A single quantitative dimension captures most but not all of the important details of the recovery process.
Background: Our study explored whether latent classes adequately represented the social capital recovery indicators at the resident level and whether latent class membership predicted subsequent exits from the recovery homes. Method: Our sample included about 600 residents in 42 recovery homes. Over a 2-year period of time, every 4 months, data were collected on eight elements of recovery capital. Results: We found 5 latent classes were optimal for representing 8 elements of recovery capital. Representing 79% of the sample, 3 of the 5 latent class profiles of the means of the 8 recovery indicators were roughly parallel and differed only in level, but the remaining 2 latent class profiles, representing 21% of the sample, were not parallel to the first 3, suggesting that a single quantitative dimension of perceived recovery may capture most but not all of the important details of the recovery process. Next, using longitudinal data from homes, the distal outcomes of resident eviction and voluntary exit were found to be related to latent class membership. Resident level pre-existing predictors (e.g., employment status, educational attainment, gender, Latinx ethnicity) and house level pre-existing predictors (e.g., financial health, poverty level of typical population served, new resident acceptance rate) significantly discriminated the classes. In a model that combined both pre-existing predictors and distal outcomes, latent class membership was still the strongest predictor of evictions controlling for the pre-existing predictors. Conclusions: These classes help to clarify the different aspects of the recovery latent score, and point to classes that have different ethnic and gender characteristics as well as outcomes in the recovery homes. For example, the high levels of self-confidence found in class 3 suggest that Latinx might be at higher risk for having some difficulties within these recovery communities.
期刊介绍:
Now in its 4th decade of publication, Substance Abuse journal is a peer-reviewed journal that serves as the official publication of Association for Medical Education and Research in Substance Abuse (AMERSA) in association with The International Society of Addiction Medicine (ISAM) and the International Coalition for Addiction Studies in Education (INCASE). Substance Abuse journal offers wide-ranging coverage for healthcare professionals, addiction specialists and others engaged in research, education, clinical care, and service delivery and evaluation. It features articles on a variety of topics, including:
Interdisciplinary addiction research, education, and treatment
Clinical trial, epidemiology, health services, and translation addiction research
Implementation science related to addiction
Innovations and subsequent outcomes in addiction education
Addiction policy and opinion
International addiction topics
Clinical care regarding addictions.