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  Healthcare Training Institute - Quality Education since 1979 CE for Psychologist, Social Worker, Counselor, & MFT!! 
  
Section 22 Question 22 | Test | Table of Contents 
 LINEAR 
MODEL Step 
1 is to clarify the threat. Many clients make vague comments that may 
or may not indicate a real danger. Thus the clinician must take the time to fully 
explore intent. For example, after an acute beating, a battered woman may state 
that she wishes someone would "blow his [the abuser's] brains out." 
In this case the clinician needs to ask the client directly whether she intends 
to kill her abuser. This client simply may be expressing her anger. Further inquiry 
might reveal that she does not own or have access to a firearm. In the above case 
the risk factor for retaliatory violence is low, especially when compared with 
the client who tells the clinician that she would like to kill her abuser and 
has borrowed her brother's loaded handgun.  Thus, if there is a clear threat, Step 2 is to assess its lethality, as well as the likelihood of the person acting on the threat. As with suicidal thoughts, not all "threats" pose a true danger or can be enacted. The incarcerated client may verbalize specific threats of violence against someone outside of prison but have no means to carry through on the threats. If there is evidence of danger, Step 3 is to identify a specific, intended victim. in family violence and family sexual assault cases, it is easy to identify intended victims. The violence is seldom random, even within homes in which multiple members reside. The clinician working with a client who is verbalizing concerns about physically and/or sexually assaulting a stranger may find it more difficult to identify a specific victim (by name). However, the clinician can ask the client to indicate the intended victim's gender and any specific victim characteristics. If the client can name the intended victim or specifics about the type of victim who will be sought, the threat of harm is imminent (Step 4). At this point the clinician needs to consider his or her duty to warn the specified victim. For more detail the reader is referred to material on the Tarasoff decisions (Tarasoff v. Regents of the University of California, 1974, 1976). The clinician also must take into account the client's relationship to the intended victim (Step 5). If the intended victim is a family member, rather than a political figure, the clinician may employ different preventive and treatment strategies. Step 6 requires the clinician to decide whether a family therapy intervention would be suitable. For example, if the family violence is ongoing, family therapy may impose greater danger to the potential victim or victims. Finally, Step 7 requires the clinician to consider whether civil commitment or involuntary hospitalization would provide the greatest good to the client and potential victim or victims. At the completion of Step 7, the clinician needs to follow up on the results of the decisions made and may need to recycle through the decision tree at a later date. The strength of the linear model is that it 
provides relatively clear direction for the clinician, as well as a "logical" 
argument for the decision. Using the linear model, the clinician approaches problem 
solving with some notion of probability. He or she weighs outcomes according to 
objective standards or theory. The weakness of this model is also its objectivity; 
contextually relevant information is given little consideration. In other words, 
factors such as treatment outcomes, social support, and stabilization of stress 
are not considered in making the prediction. The decision is driven by formula, 
more than by the specifics of the actual situation. Self-Injurious Behavior in Adolescents
  
   - Whitlock J. (2010). Self-injurious behavior in adolescents. PLoS medicine, 7(5), e1000240. doi:10.1371/journal.pmed.1000240 Personal 
  Reflection Exercise #8 Update - Lyu, J., Shi, H., Zhang, J., & Norvilitis, J. (2022). Prediction model for suicide based on back propagation neural network and multilayer perceptron. Frontiers in neuroinformatics, 16, 961588. https://doi.org/10.3389/fninf.2022.961588 
 QUESTION 
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