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According to Mechanic (1967), the layman has usually assumed that the psychiatric professional is the unbiased expert on who is, or is not, mentally ill. In addition, the psychiatric professional has also usually assumed that his labeling decisions are based upon an unbiased scientific examination of the available information. However, Mechanic, and other sociological observers of the mental illness labeling process, have recently questioned whether these assumptions are legitimate. In fact, these observers have found that the professional’s labeling decisions are apparently heavily biased in favor of mental illness. Mechanic (1967), recounting his experience at two state institutions, reported that once a person is presented for institutionalization, seldom if ever does the psychiatrist dis- agree, although the psychiatrist’s “reasons” for hospitalization may be based on different premises than the public’s. Scheff (1964a, 1964b) found that when psychiatrists work within a court commitment setting, they tended to presume the existence of mental disorder. In a detailed examination of actual court procedures and psychiatrists’ pre- commitment patient interviews, his evidence indicated that the psychiatrists’ investigations appeared biased and presumed the existence of disorder. The psychiatrists frequently chose arbitrary evidence upon which to base their decisions- -evidence that involved elaborate psychiatric straining and interpretation. Often they decided to commit the person in question even where no evidence could be found. Their offhand remarks suggested pre- judgment of the case, and the interviews were conducted with such marked haste and carelessness that they only lasted on the average about ten minutes. As one of the psychiatrist’s states: The petition cases are pretty automatic. If the patient’s own family wants to get rid of him you know there is something wrong (Scheff, 1964a, p. 410). Coffman (1961) has noted that psychiatrists usually seek only that information that is consistent with their opinions through a process he has called “discrediting”. Within the mental hospital with which he was associated, the patient’s records and history were carefully searched to provide justification for the patient’s label, while any evidence of health was usually ignored. On the other hand, Gove (1970) has disputed the veracity of these observations. He has presented evidence that psychiatric professionals do not routinely label people as mentally ill, and do not commit every person they examine to a mental institution. Gove has also questioned the scientific adequacy of the methods employed by Mechanic and Scheff. Unfortunately, Gove’s rebuttal was based upon a review of the literature, and most, if not all, of the studies in his review were not originally directed toward the question of possible professional bias. In the writer’s opinion, an adequate test of the legitimacy, or illegitimacy, of Mechanic’ (1967), Scheff’s (1964a, 1964b), or Goffman’s (1961) observations has not yet been made. If professional bias does exist in the mental illness labeling process, then two primary factors might account for Mechanic’s, Scheff’s, and Goffman’s observations. First, an individual’s work setting may be associated with various situational expectancies that might influence one’s diagnostic judgment. For example, the situational expectancies of the psychiatric professional, operating within his typical work setting, might be biased in favor of the probable appearance of a mentally ill person. If one expects to interview a mentally ill person, then one’s diagnostic judgment of that person’s mental status might be affected. Second, the psychiatric professional’s training and experience might foster an increased inclination to view people as mentally ill--a greater inclination than the non-professional. If the professional is less inclined to tolerate deviance than the non-professional, then the professional’s diagnostic judgments should be more likely to lead to the mental illness label. The primary goal of this dissertation will be to examine these two factors--situational expectancies and tolerance of deviance--and their role in the mental illness labeling process. Bias in the application of the mental illness label may be traceable to the effects of these two factors. / This thesis was digitized as part of a project begun in 2014 to increase the number of Duke psychology theses available online. The digitization project was spearheaded by Ciara Healy.

No description available.

Research in several domains has revealed that when individuals are asked to estimate the probability that their judgments are correct, they reveal an overconfidence effect. Judgments produced in decision environments such as psychodiagnosis, which are by their nature ambiguous and complex, appear to be most vulnerable to overconfidence. By implication, this phenomenon threatens the validity of clinical judgment and subjects clients to risks of flawed diagnoses and unsuitable treatments. / In an effort to identify variables implicated in judgment confidence and overconfidence, this study examined the relationship between four different inferential biases (dispositionalism, confirmationism, truncated data search, and narrow problem formulation) and diagnostic confidence in the context of a psychological assessment task. A second aspect of this study examined the effect of clinical experience on psychodiagnostic confidence. Thirty-six clinicians (18 experienced professionals and 18 clinical trainees) were individually presented a written client casefile, which was segmented and serially presented, to read and clinically interpret aloud. Analyses of participants' verbal protocols revealed that one of the four inferential biases studied (i.e., dispositionalism) accounted for a significant proportion of the variance in psychodiagnostic confidence scores. The author concludes that other clinician variables likely moderate the relationship between particular heuristics and judgment confidence. Regarding the second hypothesis, the data revealed no difference between experienced clinicians and clinical trainees in the degrees of psychodiagnostic confidence manifested in their verbal protocols. / The author proposes that effective remedies to overconfidence begin in training programs that lead students through problem-solving experiences that can invalidate facile, premature, and dubious diagnostic judgments. The author delineates a number of strategies that may be used by educators to achieve this end.

Cognitive theory of depression predicts that the illness is associated with an information processing bias that interprets ambiguous information in a mood-congruent or depressive fashion. This negative interpretative bias may serve as a vulnerability factor or maintenance mechanism for a depressive illness. The majority of studies investigating such interpretative biases rely primarily on subjective experimental methodologies (eg., evaluative feedback and self-report) that are vulnerable to experimenter demand effects, response selection biases, and the influence of autobiographical memories. In addition, the results from these studies have been mixed, leading to no firm evidence for the existence of a depression-linked interpretative bias for ambiguous material. In order to avoid the limitations that have plagued subjective research, the present study utilised two of the most promising objective physiological measures of assessing interpretation: the Rapid Serial Viewing Presentation (RSVP) procedure and the affective modulation of the human eye blink reflex. The modified RSVP experiment recorded the reaction time of participants reading a textual scenario that was composed of an opening ambiguous sentence and various emotionally valenced continuations. Interpretation of the ambiguous sentence could be inferred from the reaction time as comprehension latency is inversely related to perceived plausibility. The affective modulation experiment recorded the blink amplitudes of participants startled while performing an imagery task. Blink amplitudes are augmented by negative stimuli and inhibited by hedonic stimuli. Thus, the affective interpretation of ambiguous stimuli could be inferred from the size of the recorded blink response. The results of both experiments did not support the predictions made by cognitive theory. There was no difference in the reaction time responses to the various textual stimuli between 2 depressed outpatients and healthy controls. However, antidepressant medication did have an influence upon the ability of patients to correctly judge the plausibility of the emotionally valenced continuation sentences. With regard to the eye blink experiment, there was also no difference between the depressed outpatients and the controls in terms of size of blink amplitude to the various categories of affective stimuli. Depressive, ambiguous, and distorted stimuli did not augment blink amplitudes in healthy controls or depressed patients without social anxiety disorder. However, depressed patients with a comorbid diagnosis of social anxiety disorder did react to the ambiguous stimuli in an aversive and anxious manner as indicated by increased blink amplitudes. This may be due to the social aspect of the experimental context, which engenders fears of evaluation and performance anxiety. The eye blink procedure can therefore be compromised by group selection, as the comorbidity of anxiety and depression can confound the investigation of depression-linked interpretative biases. In addition, the failure of depressive stimuli to augment blink amplitudes may render the procedure insensitive to the selection of such biases

For years research has demonstrated a varying incidence of medical disorders manifesting with psychiatric symptoms. A relatively conservative estimate of such so called "medical masquerades" is around 10%. It is important to ascertain whether health care professionals are aware of possible medical masquerades perhaps most especially in a mental health center outpatient setting where non-medically trained clinicians are the first line therapists for treatment in the majority of cases. This study set about to find out how aware three types of health care clinicians (psychiatrists, nonpsychiatrically trained medical doctors, and non-medically trained mental health psychotherapists) are of the prevalence of medical masquerades, and whether these three types of clinicians perform differently on three types of clinical vignettes (psychiatric, somatoform, and medical masquerades). Results revealed that all health care professionals surveyed are aware that there are a percentage of medical masquerades in the clinical population. Results also revealed that the three types of clinicians performed differently on the case vignettes.

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This work is born out of previous research, conducted by this researcher, into the effects of psychiatric labelling on individuals thus differentiated. Informed by the investigative thrust of phenomenological inquiry, it is the aim herein to provide an illumination of the dramatic confrontation of the labelled individual with the classificatory branding that is his or her label. The question asked is: What is the experience of the labelled individual, and how does the label function as a ‘scientific fact’ (Kiesler, 2000) suffused within his being? In answering these questions, the researcher aims to abandon his own expectations, as is fitting with the phenomenological method, and to devote his sympathies entirely to the subjective disclosures which, it is hoped, the participants will offer. On this point, an obvious tension exists insofar as expectation and hypothesis necessarily constitute the inception of any research endeavour; and so, the notion of a complete bracketing of assumption and anticipation seems methodologically vague. The explorative impetus within this dissertation aims towards an elucidation of the effect of psychiatric diagnosis on the labelled individual, in terms of the individual’s experience of being-with-others. The impact of the offering of the label upon the individual’s interpersonal and intersubjective presence will be explored so as to establish whether psychiatric labelling unfolds as a disconnection of the individual from his co-existence with others.

In psychiatry, the Diagnostic and Statistical Manual of Mental Disorders (DSM) is the standard classification system used by clinicians to diagnose disorders. The DSM provides criteria sets that are quantifiable and directly observable measures or symptoms associated with each disorder. For classification, a minimum number of criteria must be observed and once this threshold is met, a disorder is considered to be present. For some disorders, a dimensional classification is also provided by the DSM where severity of disorder increases as the number of criteria observed increases (i.e., None, Mild, Moderate and Severe). While the criteria sets provided by the DSM are the primary assessment mechanisms used by clinicians in psychiatric disease classification, some criteria sets may have too many items making them problematic and/or inefficient in clinical and research settings. In addition, psychiatric disorders are inherently latent constructs without any direct visual or biological observation available which makes validation of psychiatric diagnoses difficult. The present dissertation proposes and applies two empirical statistical methods to address lengthy criteria sets and validation of diagnoses. The first proposal is a data-driven method packaged as a SAS Macro that systematically identifies subsets of criteria and associated cut-offs (i.e., diagnostic short-forms) that yield diagnoses as similar as possible as using the full criteria set. The motivating example is alcohol use disorder (AUD) which is a type of substance use disorder (SUD) in the DSM-5. A diagnosis of AUD is made when two or more of the 11 possible criteria associated with it are observed. Relying on data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC-III), the new methodology identifies diagnostic short-forms for AUD by: (1) maximizing the association between the sum scores of all 11 criteria with newly constructed subscales from subsets of criteria, (2) optimizing the similarity of AUD prevalence between the current DSM-5 rule and newly constructed diagnostic short-forms, (3) maximizing sensitivity and specificity of the short-forms against the current DSM-5 rule, and (4) minimizing differences in the accuracy of the short-form across chosen covariates. The second method introduces external validators of disorder into the process of identifying and validating short-forms. Each step in the first methodology uses some type of comparison (i.e., maximizing correlation, sensitivity, specificity) with the current DSM rule assuming the DSM is the best diagnostic target to use. However, the method does not itself assess the validity of the criteria-based definition but instead relies on the validity of the original diagnosis. For the second methodology, we no longer assume the validity of the current DSM rule and instead introduce the use of external validators (antecedent, concurrent, and predictive) as the target when identifying short-forms. Application of the method is again AUD and the NESARC III is used as the data source. Rather than use the binary yes/no diagnosis, we use the dimensional classification framework provided by the DSM to identify and validate subsets and associated severity cut-offs (i.e., dimensional short-forms) in a systematic way. Using each external validator separately in the process could prove difficult in determining a consensus across the validators. Instead, our methodology offers a way to combine these external validators into a singular summary measure using factor analysis that derives the external composite validator (ECV). Using NESARC-III and following principles of convergent validity, we identify dimensional short-forms that most relate to the ECV in theoretically justified ways. Specifically, we obtain nested subsets of the original criteria set that (1) maximize the association between ECV and newly constructed subscales from subsets of criteria and (2) obtain associated severity cut-offs that maximally discriminate on ECV based on R-Squared. Substance use disorders in the DSM-5 include alcohol use disorder (AUD), nicotine use disorder (NUD) and drug use disorders (DUDs). Each of these substances is associated with a single underlying SUD construct with the same 11 diagnostic criteria used across each substance and the same diagnostic classifications. Cannabis and non-medical prescription opioids are two examples of DUDs and both have recently been identified as major public health priorities. Due to their diagnostic similarity to AUD in the DSM-5, these substances were ideal to also test our methodologies. Using data from the NESARC on criteria for cannabis use disorder (CUD) and opioid use disorder (OUD), we forward applied the diagnostic short-forms that accurately replicated AUD and also applied the methods to each substance separately. Overall, the new methodology was able to identify shorter criteria sets for AUD, CUD, and OUD that yielded highly accurate diagnosis compared to the current DSM (i.e., high sensitivity and specificity). Specifically, excluding criteria “Neglected major roles to use” and/or “Activities given up to use” created no marked change in ability to diagnose or measure severity the same way as DSM-5. When applying the method for identifying the most valid dimensional short-forms using external validators, different severity cut-points compared to the current DSM-5 were found and different cut-points were found across AUD, OUD, and CUD. There were dimensional short-forms with as few as 7 criteria for AUD, CUD and OUD that demonstrated the same or better level of validity as using all 11 criteria. We discuss the implications of these findings and propose recommendations for future DSM revisions. Lastly, we review limitations and future extensions of each of our proposed methodologies.

Abstract not available