Predicting Cannabis Abuse Screening Test (CAST) Scores: A Recursive Partitioning Analysis Using Survey Data from Czech Republic, Italy, the Netherlands and Sweden

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11110%2F14%3A10287468" target="_blank" >RIV/00216208:11110/14:10287468 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1371/journal.pone.0108298" target="_blank" >http://dx.doi.org/10.1371/journal.pone.0108298</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1371/journal.pone.0108298" target="_blank" >10.1371/journal.pone.0108298</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting Cannabis Abuse Screening Test (CAST) Scores: A Recursive Partitioning Analysis Using Survey Data from Czech Republic, Italy, the Netherlands and Sweden

Popis výsledku v původním jazyce

Introduction Cannabis is Europe's most commonly used illicit drug. Some users do not develop dependence or other problems, whereas others do. Many factors are associated with the occurrence of cannabis-related disorders. This makes it difficult to identify key risk factors and markers to profile at-risk cannabis users using traditional hypothesis-driven approaches. Therefore, the use of a data-mining technique called binary recursive partitioning is demonstrated in this study by creating a classification tree to profile at-risk users. Methods 59 variables on cannabis use and drug market experiences were extracted from an internet-based survey dataset collected in four European countries (Czech Republic, Italy, Netherlands and Sweden), n = 2617. These 59 potential predictors of problematic cannabis use were used to partition individual respondents into subgroups with low and high risk of having a cannabis use disorder, based on their responses on the Cannabis Abuse Screening Test. Both

Název v anglickém jazyce

Predicting Cannabis Abuse Screening Test (CAST) Scores: A Recursive Partitioning Analysis Using Survey Data from Czech Republic, Italy, the Netherlands and Sweden

Popis výsledku anglicky

Introduction Cannabis is Europe's most commonly used illicit drug. Some users do not develop dependence or other problems, whereas others do. Many factors are associated with the occurrence of cannabis-related disorders. This makes it difficult to identify key risk factors and markers to profile at-risk cannabis users using traditional hypothesis-driven approaches. Therefore, the use of a data-mining technique called binary recursive partitioning is demonstrated in this study by creating a classification tree to profile at-risk users. Methods 59 variables on cannabis use and drug market experiences were extracted from an internet-based survey dataset collected in four European countries (Czech Republic, Italy, Netherlands and Sweden), n = 2617. These 59 potential predictors of problematic cannabis use were used to partition individual respondents into subgroups with low and high risk of having a cannabis use disorder, based on their responses on the Cannabis Abuse Screening Test. Both

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

FQ - Veřejné zdravotnictví, sociální lékařství

OECD FORD obor

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Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

PLoS ONE

ISSN

1932-6203

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

1-11

Kód UT WoS článku

000345745400063

EID výsledku v databázi Scopus

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