| dc.description.abstract | The detrimental effects of alcoholism on society have stimulated
the growth of addiction treatment centers. These programs are
characterized by low completion rates. This fact has promoted a
great deal of research aimed toward predicting treatment
completion. If those "at risk" for dropping out of programs can
be identified, they can be singled out for special consideration
which could result in their success with treatment.
Alternatively, if it can be determined that clients with certain
characteristics have a high probability of completing treatment
at specific centers, then patient characteristics can be
"matched" with the program shown to offer such people the best
opportunity for treatment completion. The majority of studies
in this area have used MMPI scales and/or combinations of
demographic variables for prediction. In general, these studies
have not been very successful or have failed to replicate. Some
reasons for this are small sample sizes, a limited number of
variables used in prediction, and lack of cross validation. The
present research addresses these problems by using large numbers
of subjects and predictor variables. Cross validation was
performed on an independent sample. Phase One subjects were
drawn from archival records; a sample of three hundred and
seventy subjects was obtained; two hundred were treatment
completers and one hundred seventy non completers. Variables
included in the analysis were; age, sex, race, education. marital status, nnuummbbeerr of dependents, employment status,
previous treatments, weeks sober prior to treatment, place of
residence, prescription medication, referring agent, self
reported reasons for referral, and the three validity and ten
standard clinical scales of the MMPI, Through discriminant
analysis, an overall successful classification rate of 65.4% was
obtained. Treatment completers were classified correctly 74.0%
and non completers 55.3%. The cross validation sample was
obtained and variables collected in the same manner as in phase
one. Data from one hundred treatment completers and eighty non
completers was collected. The discriminant function from phase
one derived an overall successful classification rate of 56.1%.
Treatment completers were classified correctly 69.0% and non
completers 40.0%. Results highlight a dramatic failure to
predict treatment dropouts. However, treatment completers could
be predicted. The relevance of this finding for treatment
matching was discussed. It was concluded that, due to the
heterogeneity of alcoholic samples, personality measures such as
the MMPI should only be used to describe population
characteristics at specific treatment centers; generalization
should not be expected. It was hypothesized that, by looking
for specific predictors at each treatment center instead of
searching for global predictors, treatment matching is feasible,
and may be very helpful in reducing dropout rates. | |
