Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Pedagosko i psiholosko testiranje i procjenjivanje spadaju među najvažnije doprinose znanosti o ponasanju nasem drustvu i pružaju temeljna i znacajna poboljsanja u odnosu na ranije postupke. Iako se ne može ustvrditi da su svi testovi dovoljno usavrseni niti da su sva testiranja razborita i korisna, postoji velika kolicina informacija koje ukazuju na ucinkovitost kvalitetnih instrumenata u onim situacijama u kojima je njihovo koristenje potkrijepljeno validacijskim podacima. Pravilna upotreba testova može dovesti do boljih odluka o pojedincima i programima nego sto bi to bio slucaj bez njihovog koristenja, a također i ukazati na put za siri i pravedniji pristup obrazovanju i zaposljavanju. Međutim, losa upotreba testova može dovesti do zamjetne stete nanesene ispitanicima i drugim sudionicima u procesu donosenja odluka na temelju testovnih podataka. Cilj Standarda je promoviranje kvalitetne i eticne upotrebe testova te uspostavljanje osnovice za ocjenu kvalitete postupaka testiranja. Svrha objavljivanja Standarda je uspostavljanje kriterija za evaluaciju testova, provedbe testiranja i posljedica upotrebe testova. Iako bi evaluacija prikladnosti testa ili njegove primjene trebala ovisiti prvenstveno o strucnim misljenjima, Standardi pružaju okvir koji osigurava obuhvacanje svih relevantnih pitanja. Bilo bi poželjno da svi autori, sponzori, nakladnici i korisnici profesionalnih testova usvoje Standarde te da poticu druge da ih također prihvate.

Standards for educational and psychological testing

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Applied Missing Data Analysis

The self-medication hypothesis of addictive disorders derives primarily from clinical observations of patients with substance use disorders. Individuals discover that the specific actions or effects of each class of drugs relieve or change a range of painful affect states. Self-medication factors occur in a context of self-regulation vulnerabilities--primarily difficulties in regulating affects, self-esteem, relationships, and self-care. Persons with substance use disorders suffer in the extreme with their feelings, either being overwhelmed with painful affects or seeming not to feel their emotions at all. Substances of abuse help such individuals to relieve painful affects or to experience or control emotions when they are absent or confusing. Diagnostic studies provide evidence that variously supports and fails to support a self-medication hypothesis of addictive disorders. The cause-consequence controversy involving psychopathology and substance use/abuse is reviewed and critiqued. In contrast, clinical observations and empirical studies that focus on painful affects and subjective states of distress more consistently suggest that such states of suffering are important psychological determinants in using, becoming dependent upon, and relapsing to addictive substances. Subjective states of distress and suffering involved in motives to self-medicate with substances of abuse are considered with respect to nicotine dependence and to schizophrenia and posttraumatic stress disorder comorbid with a substance use disorder.

The Self-medication Hypothesis of Substance Use Disorders: A Reconsideration and Recent Applications

Psychological interventions to treat mental health issues have developed remarkably in the past few decades. Yet this progress often neglects a central goal-namely, to reduce the burden of mental illness and related conditions. The need for psychological services is enormous, and only a small proportion of individuals in need actually receive treatment. Individual psychotherapy, the dominant model of treatment delivery, is not likely to be able to meet this need. Despite advances, mental health professionals are not likely to reduce the prevalence, incidence, and burden of mental illness without a major shift in intervention research and clinical practice. A portfolio of models of delivery will be needed. We illustrate various models of delivery to convey opportunities provided by technology, special settings and nontraditional service providers, self-help interventions, and the media. Decreasing the burden of mental illness also will depend on integrating prevention and treatment, developing assessment and a national database for monitoring mental illness and its burdens, considering contextual issues that influence delivery of treatment, and addressing potential tensions within the mental health professions. Finally, opportunities for multidisciplinary collaborations are discussed as key considerations for reducing the burden of mental illness.

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Rebooting Psychotherapy Research and Practice to Reduce the Burden of Mental Illness

The Clinician-Administered PTSD Scale (CAPS) is an extensively validated and widely used structured diagnostic interview for posttraumatic stress disorder (PTSD). The CAPS was recently revised to correspond with PTSD criteria in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013). This article describes the development of the CAPS for DSM-5 (CAPS-5) and presents the results of an initial psychometric evaluation of CAPS-5 scores in 2 samples of military veterans (Ns = 165 and 207). CAPS-5 diagnosis demonstrated strong interrater reliability (к = .78 to 1.00, depending on the scoring rule) and test-retest reliability (к = .83), as well as strong correspondence with a diagnosis based on the CAPS for DSM-IV (CAPS-IV; к = .84 when optimally calibrated). CAPS-5 total severity score demonstrated high internal consistency (α = .88) and interrater reliability (ICC = .91) and good test-retest reliability (ICC = .78). It also demonstrated good convergent validity with total severity score on the CAPS-IV (r = .83) and PTSD Checklist for DSM-5 (r = .66) and good discriminant validity with measures of anxiety, depression, somatization, functional impairment, psychopathy, and alcohol abuse (rs = .02 to .54). Overall, these results indicate that the CAPS-5 is a psychometrically sound measure of DSM-5 PTSD diagnosis and symptom severity. Importantly, the CAPS-5 strongly corresponds with the CAPS-IV, which suggests that backward compatibility with the CAPS-IV was maintained and that the CAPS-5 provides continuity in evidence-based assessment of PTSD in the transition from DSM-IV to DSM-5 criteria. (PsycINFO Database Record

The Clinician-Administered PTSD Scale for DSM–5 (CAPS-5): Development and initial psychometric evaluation in military veterans.

Emotion regulation (ER) has been identified as a critical factor in the development and maintenance of posttraumatic stress symptoms (PTS; Bardeen, Kumpula, & Orcutt, 2013 [Journal of Anxiety Disorders, 27, 188-196]; Marx & Sloan, 2005 [Behaviour Research and Therapy, 43, 569-583]; Nightingale & Williams, 2000 [British Journal of Clinical Psychology, 39, 243-254]). The current meta-analysis aimed to provide a thorough, quantitative examination of the associations between PTS and several aspects of ER. A search of the PsychINFO database resulted in 2557 titles, of which 57 met full inclusion criteria (the cross-sectional association between PTS symptoms and ER was reported, participants were 18 years or older, the article was written in English, and sufficient information was reported to calculate effect sizes). From the 57 studies that were included, 74 effect sizes were obtained. All studies were independently coded by two of the study authors for the following: citation, sample type, total N size (and group n's if applicable), mean age of participants, type of traumatic event, study design, PTS measure(s), ER measure(s), and effect size information. Eight random effects models were conducted: seven for individual ER strategies (e.g., rumination) and one for general emotion dysregulation. The largest effects were observed for general emotion dysregulation (r = 0.53; k = 13), rumination (r = 0.51; k = 5), thought suppression (r = 0.47; k = 13), and experiential avoidance (r = 0.40; k = 20). Medium effects were observed for expressive suppression (r = 0.29; k = 3) and worry (r = 0.28; k = 6). Significant effects were not observed for acceptance or reappraisal. Moderator analyses (sample and trauma type) were conducted for general emotion dysregulation, experiential avoidance, and thought suppression; no significant differences were observed. Findings from the current analysis suggest that several aspects of ER are associated with PTS symptoms across a variety of samples. Additionally, the current study highlights a number of limitations in the existing ER and PTS symptom literature.

Emotion Regulation and Posttraumatic Stress Symptoms: A Meta-Analysis

Importance Written exposure therapy (WET), a 5-session intervention, has been shown to efficaciously treat posttraumatic stress disorder (PTSD). However, this treatment has not yet been directly compared with a first-line PTSD treatment such as cognitive processing therapy (CPT). Objective To determine if WET is noninferior to CPT in patients with PTSD. Design, Setting, and Participants In this randomized clinical trial conducted at a Veterans Affairs medical facility between February 28, 2013, and November 6, 2016, 126 veteran and nonveteran adults were randomized to either WET or CPT. Inclusion criteria were a primary diagnosis of PTSD and stable medication therapy. Exclusion criteria included current psychotherapy for PTSD, high risk of suicide, diagnosis of psychosis, and unstable bipolar illness. Analysis was performed on an intent-to-treat basis. Interventions Participants assigned to CPT (n = 63) received 12 sessions and participants assigned to WET (n = 63) received 5 sessions. The CPT protocol that includes written accounts was delivered individually in 60-minute weekly sessions. The first WET session requires 60 minutes while the remaining 4 sessions require 40 minutes. Main Outcomes and Measures The primary outcome was the total score on the Clinician-Administered PTSD Scale for DSM-5 ; noninferiority was defined by a score of 10 points. Blinded evaluations were conducted at baseline and 6, 12, 24, and 36 weeks after the first treatment session. Treatment dropout was also examined. Results For the 126 participants (66 men and 60 women; mean [SD] age, 43.9 [14.6] years), improvements in PTSD symptoms in the WET condition were noninferior to improvements in the CPT condition at each of the assessment periods. The largest difference between treatments was observed at the 24-week assessment (mean difference, 4.31 points; 95% CI, –1.37 to 9.99). There were significantly fewer dropouts in the WET vs CPT condition (4 [6.4%] vs 25 [39.7%]; χ 2 1  = 12.84, Cramer V = 0.40). Conclusions and Relevance Although WET involves fewer sessions, it was noninferior to CPT in reducing symptoms of PTSD. The findings suggest that WET is an efficacious and efficient PTSD treatment that may reduce attrition and transcend previously observed barriers to PTSD treatment for both patients and providers. Trial Registration clinicaltrials.gov Identifier:NCT01800773

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A Brief Exposure-Based Treatment vs Cognitive Processing Therapy for Posttraumatic Stress Disorder: A Randomized Noninferiority Clinical Trial.

Clinician-Administered PTSD Scale for DSM-5

This meta-analysis summarizes the findings of outcome research on the degree to which telehealth treatments reduce posttraumatic stress disorder (PTSD)-related symptoms. In a search of the literature, 13 studies were identified for inclusion in the meta-analysis and were coded for relevant variables. A total of 725 participants were included. Results indicate that telehealth treatments are associated with significant pre- to postreduction in PTSD symptoms (d = 0.99, 95% confidence interval [CI]: 0.87–1.11, p < .001), and result in superior treatment effects relative to a wait-list comparison condition (d = 1.01, 95% CI: 0.76–1.26, p < .001). However, no significant findings were obtained for telehealth intervention relative to a supportive counseling telehealth comparison condition (d = 0.11, 95% CI: − 0.38 to 0.60, p = .67), and telehealth intervention produced an inferior outcome relative to a face-to-face intervention (d = − 0.68, 95% CI: − 0.39 to − 0.98, p < .001). Findings for depression symptom sev...

Daniel J. Lee

Papers

The Clinician-Administered PTSD Scale for DSM–5 (CAPS-5): Development and initial psychometric evaluation in military veterans.

Emotion Regulation and Posttraumatic Stress Symptoms: A Meta-Analysis

A Brief Exposure-Based Treatment vs Cognitive Processing Therapy for Posttraumatic Stress Disorder: A Randomized Noninferiority Clinical Trial.

Clinician-Administered PTSD Scale for DSM-5

Efficacy of Telehealth Treatments for Posttraumatic Stress-Related Symptoms: A Meta-Analysis