Evaluation of the Beck Depression Inventory
|✅ Paper Type: Free Essay||✅ Subject: Psychology|
|✅ Wordcount: 4254 words||✅ Published: 18th May 2020|
Beck Depression Inventory
Depression is one of the common mood disorders that interferes with the individual’s functioning across several purviews. It has been proven that Depression affects more than 300 million people around the globe, which indicates that this is a worldwide issue. According to Beck depression is describe as sadness, negative concept of one self, guilt, isolated behavior, and changes in physical activity. The prevalence of these schemes blocks the processing of positive information leading to negative thoughts which produce emotional issues and disbalance. The Beck Depression Inventory (BDI) holds status as one of the “top dozen tests in use by practicing mental health professionals (Piotrowski, 1996).
A major contributor to the advancement of psychotherapy, Aaron Beck’s and his Depression Inventory created a quick method for clinicians to identify aspects of an individual’s life that manifested itself as conflicts in living (Seligman-Reichenberg, 2016). Originally from Providence, Rhode Island, Aaron Beck graduated from Brown University in 1942 with the goal of being a graduate of the Yale School of Medicine. During his rotation in the psychiatry unit at the Cushing Veterans Administration Hospital (VA), he became intrigued but doubtful about psychoanalytic concepts. After two years of residency and his exposure to Rapaport’s ego psychology, his interest in cognition was solidified.
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His interest in suicide risk factors was a key factor in the development of the Beck Depression Inventory, originating from his discovery that some individuals with specific cognitive makeups were more vulnerable due to their predisposed condition to develop a disorder (Distinguished scientific award, 1990). He developed the BDI in 1961 based on the theory of negative distortions as the cause of depression. From a historical standpoint, depression was seen as a behavioral characteristic that induced hostility against self. On the contrary, BDI was developed based on patients’ descriptions of symptoms, and the information was used to structure a scale that corresponded to the severity of depression. Beck provided empirical evidence that the distortions were not sustainable, inaccurate, and the causes of negative thoughts against self as depicted in the historical literature review (Jackson-Koku, 2016).
Use and Format
The BDI was developed based on clinical observations of symptoms and behavioral characteristics of normal and psychiatric patients. It is used by mental health specialists “to assess the severity of a patient’s depression before clinical treatment for diagnostic purposes and with intervals during and after treatment to detect treatment progress or treatment stagnation” (Brower, 2013). The questionnaire is self-administered though trained interviewers or can be self-administered both requiring at least 10-15 minutes. Comprehension of the questionnaire requires educational levels of a fifth-sixth grade.
The self-rated scale consists of depressive symptoms such as a sense of failure, moodiness, pessimism, punishment, self-accusation, suicidal thoughts, self-dislike, fatigability, loss of libido, weight loss, indecisiveness, guilt, and self-dissatisfaction. Individual scale items range from 0-4 with 4 being a point of the continuum while 0 and 3 equate least and most respectively. The total summed range is from 0-63. High scores on the scale show higher depressive levels while low scores show minimal evidence of depression. The BDI also has two subscales known as somatic-performance and cognitive-affective. The self-rate scale incorporates psychometric properties of mean internal-consistency approximates that sum up to 0.86 for psychiatric individuals. The mean correlation of the overall score of depression is higher than 0.60. In bipolar depression clinical intervention, the BDI is used as a secondary measure of that can be tied up with rater-administered scales such as HAM-D (Martha, Chen, & Young, 2015). The answers provided for the 21 questions can be summarized in standardized cutoffs that differentiate the symptoms from the original. In this 0-13 represent minimal depression, 14-19 show mild depression, 20-28 depict moderate depression, and 29-63 indicate severe depression. These scores are used to estimate an individual’s overall depression severity, whether somatic or cognitive.
The BDI has undergone many developments, including card and computerized forms. The questionnaire was revised in 1978 and 1996 to give rise to BDI-1A and BDI-II, respectively. The BDI-II did not depend on a specific theory of depression, and it is available in several languages and is primarily used for severe depressive symptoms, often recalled after a two-week-timeline. The requirement is in line with the fourth edition of the diagnostic and statistical manual (Jackson-Koku, 2016). Conveniently, the questionnaire also has a shorter version known as BDI Fast Screen for Medical Patients (BDI-FS). The BDI-FS is used for primary care delivery, and it composes seven self-reporting items that correspond to primary depressive symptoms identified within the initial two weeks. Beck also developed a triad that was used to analyze negative cognitions about the future, self, and the whole world to give insight into depression. Using this triad, researchers were able to prove that intrusive negative cognition can sustain depression; the concept is used in cognitive behavioral therapy (CBT) in modern medicine.
The 21 items in the BDI are ranked on a 0-4-point scale to show the presence or absence of depression symptoms. The items cover cognitive, somatic, affective, and vegetative symptoms based on DSM-IV criteria used to diagnose depression. The scores are determined by adding the highest ratings obtained from the answers to the 21 items. 0 is the minimum score, while 63 is the maximum score. High scores are reflective of severe depressive symptoms and vice versa during clinical trials. The items express feelings t such as low self-worth, guilt, and suicidal thoughts that are common among populations suffering from depression. The set of questions incorporate 10 negative and positive statements that can be confusing to patients (Thorntorn & Argoff, 2009).
The items that incorporated changes in hypochondriasis, body image, and work difficulties were removed from BDI-11. The revisions on BDI-II also included items on appetite and sleep loss were made to include decrement an increment of the same. The questionnaire also has items that deal with feelings such as suicidal thoughts, sex interest, and self-punish. The BDI is useful in clinical trials since it shows the closeness of scores to the magnitude of depression. The items have been crucial elements of clinical trials in psychiatry and psychology fields. The result obtained from answering the items can be used in the quantitative assessment of levels of depression. In this regard, medical practitioners can monitor changes within their patients for certain timelines before administering appropriate treatment.
Content validity of the BDI has shown remarkable improvements due to the replacements and recordings made on it to align with DSM-IV criteria. Mean correlation coefficients (0.72 and 0.60) have been proven to be with clinical ratings of depressive symptoms for both psychiatric and non-psychiatric individuals. In clinical trials, construct validity is high for depression symptoms when the questionnaire is used; that is, α=0.92 for psychiatric persons and 0.93 for college-going individuals (Jackson-Koku, 2016). Also, empirical evidence shows that concurrent validities can be demonstrated between the questionnaires and other related metrics of depression. For example, Minnesota Multiphasic Personality Inventory-D established that γ = 0-77.
Besides, studies on criterion validity of BDI-II has a positive correlation with the Hamilton Depression Rating Scale that showed that γ = 0.71 and the test-retest reliability for a one-week-timeline as γ = 0.93. The empirical evidence above demonstrates the robustness of the questionnaire when it is used to measure the daily variations of patients’ feelings; therefore, its internal consistency α = 91. The internal consistency exhibited by each item on the self-rate scale using the Cronbach’s α coefficient produced similar results (Batista et al., 2018). In this regard, the questionnaire incorporates the dimensionality of the items to separate somatic and cognitive domains. Besides, results from BDI-II can be conceptualized based on affective, somatic, and cognitive symptoms. The variance of the symptoms has also been accurately used to determine the severity of depression. Therefore, the BDI-II total score, as well as subscale factors, has shown acceptable internal consistency with α ranging from 0.70- 0.89. T-test analysis of the results has been demonstrated that BDI scores can be differentiated into hospitalized persons and patients at home. Collectively, the results provide an explanatory framework of BDI based DSM-IV criteria used to evaluate the severity of depression.
Several studies have been made to prove the reliability of the BDI instrument and its ability to quantify depression. In one of the studies made by the Ability Lab to non-specific patient population, the result of test/retest reliability for non-psychiatric college-aged subjects administrated in English and Spanish was adequate (ICC= 0.73). The study stated that there was no difference between scores on the English and Spanish versions, however for the psychiatric outpatient the reliability result was excellent with a coefficient alpha =0.92, which leaded to an internal consistency between a Cronbach’s alpha =0.81 and 0.86.
In this study the content validity of BDI meets three important elements: it created a mirror to the criteria for clinical depression outlined in Diagnostic and Statistical Manual of Mental Disorders, 4th edition. Also, it reflected six of nine criteria used on the DSM-III for a diagnosis of depression. Lastly it was constructed from a clinical consensus about depressive symptoms.
Advantages and Disadvantages of the Application of BDI-II (latest edition)
According to the National Child traumatic Stress Network the BDI-II is a great tool for testing, not diagnosing depression. In different studies that they made with different ethnic groups, gender, and range in ages, some advantages and disadvantages where discover and will need to be take in consideration at the time of administrating assessment to certain groups.
- The BDI-II is widely used and accepted as a measure of depressive symptomatology.
- The BDI-II can be administered orally by an examiner to those with reading difficulties or problems with concentration.
- The BDI-II is user-friendly; it is easy to administer and score.
- It has been translated into languages other than English, and its psychometric properties have been established in numerous cultural groups including the deaf population.
- The BDI-II is designed to assess state-related depression and could be used as a quick weekly screener prior to therapy sessions.
- The measure has been found to be useful in detecting change in treatment-outcome studies.
- Due to the face validity of the BDI-II, underreporting and overreporting may be likely.
- Individuals with low education and some Spanish speakers have difficulty with the response format.
- The procedure used to determine the cut scores may increase the likelihood of false positives or overdiagnoses of depression among clients.
- The wording in some items asks the respondent to compare their current state to a prior one (e.g., than usual, as ever). Individuals with chronic trauma since childhood sometimes respond by circling a zero because they do not feel worse than “usual.”
- The normative sample is predominantly White (91%).
- Although the measure can be used for adolescents, the norms were gathered with adults.
- The majority of psychometric studies conducted with adolescents in the United States have involved predominantly Caucasian samples and have not included large numbers of individuals of lower socio-economic status. More research is needed on the use of the BDI-II with diverse groups of adolescents.
There are different kits, manuals, interpretive reports for self-administration that can be acquired online in diverse price ranges. (Beck, A. T., MD, Steer, R. A., & Brown, G. K 1996)
Q-Global Web-based Administration, Scoring and Reporting
Starter Kit: $96.00
Digital Manual: 57.00
Interpretive Report: $3.10
Online Report subscriptions can vary in price, there prices are between $40.00 and 165.00 depending on how many years the individual wants to have the subscription with unlimited use for a single user.
Record forms English or Spanish: $61.75
Described as the “gold standard” for comparing psychological devices, the Beck Depression inventory is commonly used to validate results of other tests (Hammond, 1995). It is also favored for research objectives in the study of diagnosing depression in chronic pain patients, identifying the role of depression in the excessive use of internet games in colleges students, and correlating depression with hearing loss in the elderly.
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Depression and pain coexist with overlapping symptoms of change in activity, insomnia, and fatigue. The following study and findings were conducted by Peter Knaster, Ann-Marie Estlander, Hasse Karlsson, Jaakko Kaprio, and Eija Kalso to assess BDI items encompassing somatic and cognitive-emotions in relation to diagnoses of depression, disability, and pain levels to validate its effectiveness in diagnosing depression without the influence of pain. Along with a pain questionnaire and the SCID DSM-IV interview for psychiatric assessment, the BDI was administered by dividing the 21 items into categories that fell into negative view of self and somatic and physical function factors. This was performed to isolate those cognitive identifiers of depression, like guilt, self-dislike, and worthlessness from those related to chronic pain. The study was administered to 121 patients who had experienced chronic pain for at least a year but were not taking opioid medications, medications for psychosis, and were not experiencing any type of malignancy.
Results substantiated the idea that feelings of self-negativity are minimal in chronic pain depression than depression attributed to diagnoses of Major Depressive Disorders (MDD). BDI scores were high in patients with MDD and validated the relationship between somatic symptoms and pain related depression (Knaster, Estlander, Karlsson, Kaprio, Kalso, 2016). This study validates its use as a mechanism of finding baseline indicators of depression to ensure encompassing care for all issues are addressed and treated.
Online gaming is a common recreational activity in most homes in the United States and part of most adolescents’ lives, many of them forgoing interactions outside for role playing games with competitive interactions inside. In a study by Partha Malakar, Anuja Chakravorty, and Debasish Sanyal, exploration of depression and its correlation with excessive gaming along with effects of cyberbullying, xenophobia, and violence influencing tested using the BDI. This study was conducted in anticipation of a society of increased capacity to use internet games which may result in depression.
The BDI was administered to 200 college students, 100 males and 100 females, from the age of 18-22 with no history or current indications of physical and mental complications. This data was compared with participant data from the Internet Gaming Disorder Test and correlations were made between hours of gaming and increase feelings of depression along with the susceptibility of gaming depression based on sex.
According to the study, depression results ranked high in participants who scored high on the BDI and the IGD but low for participants categorized as “low gamers” on the IGD. The difference between scoring between the two was significant indicating that the amount of time on gaming systems has a substantial impact on an individual’s quality of life. Additionally, the interaction between gaming and sex were insignificant in regard to developing depression (Malakar, Chakravorty, Sanyal, 2016). Depression impacts both sexes in the instance of extreme gaming.
One of the most common sensory impairments to occur with gradual aging is hearing loss. Presbycusis is the loss of auditory functions and is not discriminatory, essentially affecting people from around the world. A gradual decrease in a person’s health can have a significant impact on their quality of living and many depressive episodes can be attributed to adjusting to medical devices for daily activities, in this instance hearing aids. To test this theory, a presbycusis study on the elderly population experiencing hearing impairments and requiring hearing aids was conducted by members of the Department of Audiology at the Isfahan University of Medical Sciences in Isfahan, Iran.
Participants, 35 total, were over 60 years old in age, free of diabetes and hypertension and consisted of 19 men and 16 women. Each participant was given the BDI-II with data being analyzed by researchers and categorized by severity. The results indicated a substantial correlation between hearing loss and depression in the clients with 8.6% having minimal depression, 14.3% experiencing mild depression, 20.0% with moderate depression, and an astounding 57.1% with severe depression (Nilforoush, Sepehrnejad, Habibi, 2017). The study concludes that future use of the BDI-II during geriatric examinations and rehabilitation process will drastically increase improvement in early exposure to therapeutic services to combat resulting depression.
The Beck Depression Inventory is a widely used reporting system that has truly made a significant impact on the ability to provide quality therapeutic care to affected individuals. With its simple design and quick access to assessment results, the BDI offers mental health professionals objective data on the status of their clients to treat or provide preventive measures for depression. Initially created by Aaron T. Beck in 1961, the BDI gone through many modifications but still remains a reliable study for both clinical use and use in clinical research. It is the “golden standard” in the utilization of a multitude of surveys across the psychiatric realm.
- Batista, E. Z., Pena, K. G., Vindel, C. A., Martinez, H. X., & Medrano, A. L. (2018). Validity and reliability of Beck Depression Inventory (BDI-II) in general and hospital population of Dominican Republic. PLOS ONE, 1-12. doi: https://doi.org/10.1371/journal.pone.0199750
- Beck, A. T., MD, Steer, R. A., & Brown, G. K. (1996). Beck Depression Inventory-II. Retrieved July 6, 2019, from https://www.pearsonassessments.com/store/usassessments/en/Store/Professional-Assessments/Personality-%26-Biopsychosocial/Beck-Depression-Inventory-II/p/100000159.html
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- Contreras-Valdez, José Alfredo, Hernández-Guzmán, Laura, & Freyre, Miguel-Ángel. (2015). Validez de constructo del Inventario de Depresión de Beck II para adolescentes. Terapia psicológica, 33(3), 195-203. doi: https://dx.doi.org/10.4067/S0718-48082015000300004
- Distinguished scientific award for the applications of psychology: Aaron T Beck (1990). American Psychologist, 45(4), 458-460. https://doi-org.waylandbu.idm.oclc.org/10.1037/h0091609
- Hammond, S.M. (1995). An IRT investigation of the validity of non-patient analogue research using the Beck Depression Inventory. European Journal of Psychological Assessment, 11(1), 14-20. https://doi-org.wayland.idm.oclc.org/10.1027/1015-57220.127.116.11
- Jackson-Koku, G. (2016). Beck Depression Inventory: Questionnaire review. Occupational Medicine, 66(1), 174-175.
- Knaster, P., Estlander, A.-M., Karlsson, H., Kaprio, J., &Kalso, E. (2016). Diagnosing Depression in Chronic Pain Patients: DSM-IV Major Depressive Disorder vs. Beck Depression Inventory (BDI). PLoS ONE, 11(3), 1-9. https://doi-org.waylandbu.idm.oclc.org/10.1371/journal.pone.0151982.
- Malakar, P., Chakravorty, A., & Sanyal, D. (2019). Role of excessive use of internet games on anxiety and depression among college students. International Journal of Social Sciences Review, 7(3), 388–392 http://search.ebscohost.com.waylandbu.idm.oclc.org/login.aspx?direct=true&db=sih&A
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- Nilforoush, M. H., Sepehrnejad, M., & Habibi, Z. (2017). Beck Depression Inventory-II in Hearing Impaired Elderly Patients: A Presbycusis Study. Indian Journal of Otology, 23(3), 168–170. https://doi-org.waylandbu.idm.oclc.org/10.4103/indianjotol.INDIANJOTOL_63_16
- Piotrowski, Chris. (1996). Use of Beck Depression Inventory in Clinical Practice. Psychological Reports, Vol 79(3, Pt 1), pp. 873-874. http://dx.doi.org.waylandbu.idm.oclc.org/10.2466/pr0.1918.104.22.1683
- Ryan, S. (2012, April 26). Beck Depression Inventory. Retrieved July 6, 2019, from https://www.sralab.org/rehabilitation-measures/beck-depression-inventory#non-specific-patient-population
- Seligman, L., & Reichenberg, L. W. (2016). Theories of counseling and psychotherapy: Systems, strategies, and skills. Tamil Nadu, India: Pearson India Education Services.
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- Wiebe, J. S., & Penley, J. A. (2005, December). A psychometric comparison of the Beck Depression Inventory-II in English and Spanish. Retrieved July 6, 2019, from https://www.ncbi.nlm.nih.gov/pubmed/16393015
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