Predicting addiction severity index (asi) interviewer severity ratings for a computer-administered asi

Stephen F. Butler, John S. Cacciola, Simon H. Budman, Sabrina Ford, David Gastfriend, Ihsan M Salloum, Frederick L. Newman, Arlene Frank, A. Thomas McLellan, Jack Blaine, Karla Moras, Jacques P. Barber

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

The Addiction Severity Index (ASI) is a reliable and valid measure of problem severity among addicted patients. Concerns have been raised about the reliability of the Interviewer Severity Rating (ISR), a summary score for each of 7 domains. As part of an effort to build a computer-administered ASI, regression equations were developed to predict the ISR. Repeated resampling of a large dataset, consisting of 1,124 ASIs conducted by trained interviewers, permitted derivation of stable regression equations predicting the ISR for each ASI domain from patients' answers to preselected interview items. The resulting 7 Predicted Severity Ratings (PSRs) were tested on 8, standardized vignettes, with 'gold standard,' expert-generated ISRs. Reliabilities compared well with those of intensively trained interviewers. The PSRs could provide an alternative to potentially unreliable interviewer ratings, enhancing the ASI's role in treatment planning and treatment matching and make possible a computer-administered version of the ASI.

Original languageEnglish
Pages (from-to)399-407
Number of pages9
JournalPsychological Assessment
Volume10
Issue number4
DOIs
StatePublished - Dec 1 1998
Externally publishedYes

Fingerprint

Interviews
Therapeutics

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Cognitive Neuroscience
  • Applied Psychology

Cite this

Predicting addiction severity index (asi) interviewer severity ratings for a computer-administered asi. / Butler, Stephen F.; Cacciola, John S.; Budman, Simon H.; Ford, Sabrina; Gastfriend, David; Salloum, Ihsan M; Newman, Frederick L.; Frank, Arlene; McLellan, A. Thomas; Blaine, Jack; Moras, Karla; Barber, Jacques P.

In: Psychological Assessment, Vol. 10, No. 4, 01.12.1998, p. 399-407.

Research output: Contribution to journalArticle

Butler, SF, Cacciola, JS, Budman, SH, Ford, S, Gastfriend, D, Salloum, IM, Newman, FL, Frank, A, McLellan, AT, Blaine, J, Moras, K & Barber, JP 1998, 'Predicting addiction severity index (asi) interviewer severity ratings for a computer-administered asi', Psychological Assessment, vol. 10, no. 4, pp. 399-407. https://doi.org/10.1037/1040-3590.10.4.399
Butler, Stephen F. ; Cacciola, John S. ; Budman, Simon H. ; Ford, Sabrina ; Gastfriend, David ; Salloum, Ihsan M ; Newman, Frederick L. ; Frank, Arlene ; McLellan, A. Thomas ; Blaine, Jack ; Moras, Karla ; Barber, Jacques P. / Predicting addiction severity index (asi) interviewer severity ratings for a computer-administered asi. In: Psychological Assessment. 1998 ; Vol. 10, No. 4. pp. 399-407.
@article{98b612d2a40f4c1c8187b6c75829cfc3,
title = "Predicting addiction severity index (asi) interviewer severity ratings for a computer-administered asi",
abstract = "The Addiction Severity Index (ASI) is a reliable and valid measure of problem severity among addicted patients. Concerns have been raised about the reliability of the Interviewer Severity Rating (ISR), a summary score for each of 7 domains. As part of an effort to build a computer-administered ASI, regression equations were developed to predict the ISR. Repeated resampling of a large dataset, consisting of 1,124 ASIs conducted by trained interviewers, permitted derivation of stable regression equations predicting the ISR for each ASI domain from patients' answers to preselected interview items. The resulting 7 Predicted Severity Ratings (PSRs) were tested on 8, standardized vignettes, with 'gold standard,' expert-generated ISRs. Reliabilities compared well with those of intensively trained interviewers. The PSRs could provide an alternative to potentially unreliable interviewer ratings, enhancing the ASI's role in treatment planning and treatment matching and make possible a computer-administered version of the ASI.",
author = "Butler, {Stephen F.} and Cacciola, {John S.} and Budman, {Simon H.} and Sabrina Ford and David Gastfriend and Salloum, {Ihsan M} and Newman, {Frederick L.} and Arlene Frank and McLellan, {A. Thomas} and Jack Blaine and Karla Moras and Barber, {Jacques P.}",
year = "1998",
month = "12",
day = "1",
doi = "10.1037/1040-3590.10.4.399",
language = "English",
volume = "10",
pages = "399--407",
journal = "Psychological Assessment",
issn = "1040-3590",
publisher = "American Psychological Association Inc.",
number = "4",

}

TY - JOUR

T1 - Predicting addiction severity index (asi) interviewer severity ratings for a computer-administered asi

AU - Butler, Stephen F.

AU - Cacciola, John S.

AU - Budman, Simon H.

AU - Ford, Sabrina

AU - Gastfriend, David

AU - Salloum, Ihsan M

AU - Newman, Frederick L.

AU - Frank, Arlene

AU - McLellan, A. Thomas

AU - Blaine, Jack

AU - Moras, Karla

AU - Barber, Jacques P.

PY - 1998/12/1

Y1 - 1998/12/1

N2 - The Addiction Severity Index (ASI) is a reliable and valid measure of problem severity among addicted patients. Concerns have been raised about the reliability of the Interviewer Severity Rating (ISR), a summary score for each of 7 domains. As part of an effort to build a computer-administered ASI, regression equations were developed to predict the ISR. Repeated resampling of a large dataset, consisting of 1,124 ASIs conducted by trained interviewers, permitted derivation of stable regression equations predicting the ISR for each ASI domain from patients' answers to preselected interview items. The resulting 7 Predicted Severity Ratings (PSRs) were tested on 8, standardized vignettes, with 'gold standard,' expert-generated ISRs. Reliabilities compared well with those of intensively trained interviewers. The PSRs could provide an alternative to potentially unreliable interviewer ratings, enhancing the ASI's role in treatment planning and treatment matching and make possible a computer-administered version of the ASI.

AB - The Addiction Severity Index (ASI) is a reliable and valid measure of problem severity among addicted patients. Concerns have been raised about the reliability of the Interviewer Severity Rating (ISR), a summary score for each of 7 domains. As part of an effort to build a computer-administered ASI, regression equations were developed to predict the ISR. Repeated resampling of a large dataset, consisting of 1,124 ASIs conducted by trained interviewers, permitted derivation of stable regression equations predicting the ISR for each ASI domain from patients' answers to preselected interview items. The resulting 7 Predicted Severity Ratings (PSRs) were tested on 8, standardized vignettes, with 'gold standard,' expert-generated ISRs. Reliabilities compared well with those of intensively trained interviewers. The PSRs could provide an alternative to potentially unreliable interviewer ratings, enhancing the ASI's role in treatment planning and treatment matching and make possible a computer-administered version of the ASI.

UR - http://www.scopus.com/inward/record.url?scp=0032407316&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032407316&partnerID=8YFLogxK

U2 - 10.1037/1040-3590.10.4.399

DO - 10.1037/1040-3590.10.4.399

M3 - Article

AN - SCOPUS:0032407316

VL - 10

SP - 399

EP - 407

JO - Psychological Assessment

JF - Psychological Assessment

SN - 1040-3590

IS - 4

ER -