6 The DSM IV is a diagnostic manual used by clinicians for both adults and children (American Psychiatric Association, 1994). Its most important feature is the provision of diagnostic criteria which is intended to improve the reliability of diagnostic judgements. The manual uses multiaxial assessment which involves assessment of the patient on several axes, " each of which refers to a different domain of information that may help the clinician plan treatment and predict outcome" (p.3?). In the DSM the multiaxial classification system uses five axes: Axis I - Clinical Disorders and Other Conditions That May Be A Focus of Clinical Attention (e.g., mood or anxiety disorders), Axis IT - Personality Disorders and Mental Retardation, Axis III - General Medical Conditions (e.g., genitourinary and metabolic disorders), Axis IV - Psychosocial and Environmental Problems and Axis V - Global Assessment of Functioning. There are several subcategories within Axis IV assessment, including that of Parent-Child Relational Problem. This problem is fOlmd under the generic heading "Relational Problems." This category should be used when the focus of clinical attention is on a pattern of interaction between the parent and child (e.g., impaired communication) that is related to clinically significant impairment in individual or family functioning or the development of clinically significant symptoms in parent and/or child (American Psychiatric Association, 1 994). Finally, Axis V is for reporting the clinician' s judgement of the patient's overall level of functioning and is based on a scale score from 0 to 1 00.
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Instead of next meeting at case
#50
as initially agreed, both researchers met at case#3 1
due to some difficulties being raised about coding. Difficulties seemed to reflect the fact that the assessments were extremely varied in both amount and depth of information provided. The following concerns were raised at this stage:(a) interpretation of support information, and (b) ratings for level of negative support severity.
With respect to both positive and negative emotional support, it was found that whilst clinicians may mention a relationship between the patient and a friend or school
counsellor, they often did not give much detail as to whether the client actually perceived this relationship as helpful or not. We agreed that if there was reference to a relationship (e.g., several friends), but no reference to whether it was supportive, the details would be counted only under the quantity section (e.g., several friends =
medium-sized group). If information was scarce in the clinical report (e.g., mention of friends only) and made no reference to either quantity or quality, it was agreed that no indication of support would be made in the data collected. There were only a few instances where this resolution had to be applied and as such the effect on analysis outcomes would have been minimal.
There were a few instances where scarcity of information made it somewhat difficult to make a decision with respect to negative support severity. For instance, one report might indicate that "there was violence in X's background", whereas another file may say "patient
Y
experienced physical abuse from the age of3
to present and this included both witnessing and experiencing the abuse". Based on this information, onewould conclude, using the severity rating scale, that patient
Y
might receive a rating ofmediwn severity, but patient X could either receive a rating of low, medium or high depending on the type of abuse and the extent of exposure to the abuse. Both researchers agreed that they would err on the conservative side in these situations. As such, patient X would receive a severity rating of medium, depending of course upon whether there was any mention of the degree of impairment that the abuse had on the client. Low severity ratings were mainly assigned to those situations where there was conflict, but no abuse or trauma.3.5
Data Analysis
The tests used for this study were chosen as a result of the nature of the data collected. The data were collected from a non-random sample and largely consisted of the collection of categorical data, thereby necessitating the use of non parametric testing (e.g.,
X2)
(Bordens& Abbott, 1 996). For the numerical and scale data, t-tests, means
and standard deviations were computed using theSPSS 8.0
program. Interrater reliability statistics were computed for all categorical and numerical data..A multivariate procedure, also known as a loglinear model, was utilised in order to measure the association between multiple levels of categorical data.. Loglinear methods allow the researcher to test differences between groups analogous to the ANOVA procedure (SPSS Advanced Statistics 7. 5, 1 997). In addition, the loglinear method allows the researcher to identifY relationships between multiple layers of categorical data.. The loglinear model basically constructs a multivariate contingency table. This table enables the investigation of the relationship between the variables,
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treating all the variables used in the table as independent variables, with the dependent variables being the number of cases located in each cell of the contingency table (e.g., the variable which is predicted by other variables). The outcome table is a linear model which is developed as a result of this analysis and enables cell frequencies to be predicted. The better the predictive utility of the model, the closer the expected frequency is to the observed frequency. The principle behind the construction of a loglinear model is very similar to that for x.2 in that the observed frequencies and the expected frequencies are compared cell by cell. However, unlike x.2, loglinear analysis enables researchers to explore higher-order interaction effects between several
different variables (Cl ark-Carter,
1 997).
The loglinear model analysis, using a backward elimination strategy, was chosen for this study. This means that the model selection procedure begins with a fully saturated model and removes the variables that do not contribute significantly to the model. The backward elimination strategy eliminates the effect( s) with the least predictive value (i.e., largest probability, provided that the probability is greater than
0.05 ) (SPSS
Advanced Statistics
7.5,1 997).
One of the assumptions of log linear model analysis is independence of the categories contained within the variables. That is, the categories within the variables must be mutually exclusive to one another and exhaustive (similar to the chi-square). The data collected for the variables in this study meet the assumption of independence for the loglinear analysis, since ratings could not belong simultaneously to two or more categories in any given variable. It is important to note that it is not necessary for the