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A refreshing and necessary start: Commentary on Westra and Di Bartolomeo (2024).
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| Pengarang | : | Marcus |
| Nama Majalah/Jurnal | : | American Psychologist |
| Volume / Edisi | : | 79 (No. 2) |
| Halaman | : | 179-181 |
| Abstrak | : | Westra and Di Bartolomeo (2024) make a compelling case for integrating “process acuity” skills into routine clinical training. The authors have done the field a service by articulating the need for novel and process-science-informed psychotherapy training and practice. This brief commentary echoes the authors’ observations about the status quo of clinical training and expands upon what else will need to be considered to fully realize the goals of their proposal. Specifically, research on negative process signals has outpaced research on the optimal responses to such process signals. Further, process–outcome associations and the relevance of other technical mechanisms vary by the therapist/trainee (and dyad), which will require enhanced training personalization and precision. Future work on implementation will require elucidating responsive clinical strategies based on the accumulation of more sophisticated and contextualized process–outcome and training research. (PsycInfo Database Record (c) 2024 APA, all rights reserved) |
Msalah Kesukuan di Sri Lanka
| Pengarang | : | Budiman Gultom |
| Nama Majalah/Jurnal | : | Ilmu dan Budaya |
| Volume / Edisi | : | X-2, NOVEMBER (No. 2) |
| Halaman | : | 119-128 |
| Abstrak | : | - |
Sistem Managemen Peningkatan Mutu Terpadu (Total Quality Control) Melalui Gugus Kendali Mutu (Quality Control Circle) ke Arah Peningkatan Produktivitas
| Pengarang | : | Mimien Saleh |
| Nama Majalah/Jurnal | : | Ilmu dan Budaya |
| Volume / Edisi | : | X-2, NOVEMBER (No. 2) |
| Halaman | : | 112-118 |
| Abstrak | : | - |
Process-focused psychotherapy training is evidence-based psychotherapy training: Commentary on Westra and Di Bartolomeo (2024).
| Pengarang | : | Arie Raharjo, Argohartono |
| Nama Majalah/Jurnal | : | American Psychologist |
| Volume / Edisi | : | 79 (No. 2) |
| Halaman | : | 175-178 |
| Abstrak | : | Westra and Di Bartolomeo (2024) made a strong case for psychotherapy training to focus more on psychotherapy process rather than content (e.g., manuals). Their recommendations are consistent with the preponderance of the evidence that finds that psychotherapy process variables (and psychotherapist variables related to the process, such as empathy) account for most of the variance in psychotherapy outcomes. Despite the overwhelming evidence, the Great Psychotherapy Debate (Wampold & Imel, 2015) rages on. In this commentary, I emphasize and strive to extend Westra and Di Bartolomeo’s recommendations. I describe how and when we might time the training of process monitoring to be most effective in the context of trainee development, and how such process monitoring may be useful both generally, as well as for cultural responsiveness. I begin, however, by addressing the substantial barriers we face in the integration of process-based training approaches given the prevailing avoidance of process. As the authors note, despite the proliferation of psychotherapy models and manuals, client outcomes have not improved in kind. If the field of psychotherapy is to make progress in helping to address the ongoing mental health crisis, it will necessitate that we finally heed the scientific evidence and orient our practice and our training (as Westra & Di Bartolomeo, 2024, recommend) to the process of psychotherapy. |
Developing expertise in psychotherapy: The case for process coding as clinical training
| Pengarang | : | Henny A. Westra and Alyssa A. Di Bartolomeo |
| Nama Majalah/Jurnal | : | American Psychologist |
| Volume / Edisi | : | 79 (No. 2) |
| Halaman | : | 163-174 |
| Abstrak | : | Routine outcome monitoring (ROM) is a major development in the field since it offers likely outcome trajectories and is particularly helpful for failing cases. However, ROM has not led to improved skill development more generally, and it is debatable as to whether expertise is even possible to acquire in psychotherapy. What is missing but crucial to expertise is feedback on the outcome of one’s actions in real time, which would enable responsive adjustments and improve outcomes. It is argued in this article that by identifying empirically validated moment-to-moment markers capable of differentiating later clinical outcomes, process researchers have uncovered the possibility of extracting prognostic information in real time, but one must develop the requisite observational skills. Multiple lines of research are reviewed to support the contention that real-time outcome information is available to guide responsivity and improve outcomes. And the typically hidden nature of these important signals further underscores the need for systematic training in process acuity. Given the pressing need to improve training methods, process coding training should not be restricted to research laboratories but should be exported to the clinical setting and tailored to the needs of clinicians for use in real time during therapy sessions. These are testable hypotheses that, if successful, hold the possibility of improving training and reversing the worrying trend of experience in psychotherapy being unrelated to outcome. |
Persons or data points Ethics, artificial intelligence, and the participatory turn in mental health research.
| Pengarang | : | Skorburg, Joshua August; O'Doherty, Kieran; Friesen, Phoebe. |
| Nama Majalah/Jurnal | : | American Psychologist |
| Volume / Edisi | : | 79 (No. 1) |
| Halaman | : | 137-149 |
| Abstrak | : | This article identifies and examines a tension in mental health researchers’ growing enthusiasm for the use of computational tools powered by advances in artificial intelligence and machine learning (AI/ML). Although there is increasing recognition of the value of participatory methods in science generally and in mental health research specifically, many AI/ML approaches, fueled by an ever-growing number of sensors collecting multimodal data, risk further distancing participants from research processes and rendering them as mere vectors or collections of data points. The imperatives of the “participatory turn” in mental health research may be at odds with the (often unquestioned) assumptions and data collection methods of AI/ML approaches. This article aims to show why this is a problem and how it might be addressed. |
Expanding the role of justice in secondary research using digital psychological data
| Pengarang | : | Herington, Jonathan; Li, Kevin; Pisani, Anthony R |
| Nama Majalah/Jurnal | : | American Psychologist |
| Volume / Edisi | : | 79 (No. 1) |
| Halaman | : | 123-136 |
| Abstrak | : | Secondary analysis of digital psychological data (DPD) is an increasingly popular method for behavioral health research. Under current practices, secondary research does not require human subjects research review so long as data are de-identified. We argue that this standard approach to the ethics of secondary research (i.e., de-identification) does not address a range of ethical risks and that greater emphasis should be placed on the ethical principle of justice. We outline the inadequacy of an individually focused research ethic for DPD and describe unaddressed “social risks” generated by secondary research of DPD. These risks exist in the “circumstances of justice”: that is, a circumstance where individuals must cooperate to create a public good (e.g., research knowledge), and where it is impractical to individually exempt individuals. This requires researchers to emphasize the just allocation of benefits and burdens against a background of social cooperation. We explore six considerations for researchers who wish to conduct research with DPD without explicit consent: (a) create socially valuable knowledge, (b) fairly share the benefits and burdens of research, (c) be transparent about data use, (d) create mechanisms for withdrawal of data, (e) ensure that stakeholders can provide input into the design and implementation of the research, and (f) responsibly report results. |
Visual Digital Data, Ethical Challenges, and Psychological Science
| Pengarang | : | Mark Levine, Richard Philpot, Sophie J. Nightingale, and Anastasia Kordoni |
| Nama Majalah/Jurnal | : | American Psychologist |
| Volume / Edisi | : | 79 (No. 1) |
| Halaman | : | 109-122 |
| Abstrak | : | Digital visual data afford psychologists with exciting research possibilities. It becomes possible to see real-life interactions in real time and to be able to analyze this behavior in a fine-grained and systematic manner. However, the fact that faces (and other personally identifying physical characteristics) are captured as part of these data sets means that this kind of data is at the highest level of sensitivity by default. When this is combined with the possibility of automatic collection and processing, then the sensitivity risks are compounded. Here we explore the ethical challenges that face psychologists wishing to take advantage of digital visual data. Specifically, we discuss ethical considerations around data acquisition, data analysis, data storage, and data sharing. We begin by considering the challenges of securing visual data from both public space security systems and social media sources. We then explore the dangers of bias and discrimination in automatic data processing, as well as the dangers to human analysts. We set out the ethical requirements for secure data storage, the dangers of “function creep,” and the challenges of the right of the individual to withdraw from databases. Finally, we consider the tensions that exist between sensitive visual data that require extra protections and the recent open science movement, which advocates data transparency and sharing. We conclude by offering a practical route map for tackling these complex ethical issues in the form of a Privacy and Data Protection Impact Assessment template for researchers. |
On Conducting Ethically Sound Psychological Science in the Metaverse
| Pengarang | : | Cockerton, Tracey; Zhu, Ying; Dhami, Mandeep K. |
| Nama Majalah/Jurnal | : | American Psychologist |
| Volume / Edisi | : | 79 (No. 1) |
| Halaman | : | 92-108 |
| Abstrak | : | As the next generation of the internet, the metaverse is an immersive three-dimensional (3D) world that incorporates both physical and virtual environments. The metaverse affords numerous advantages for advancing our theoretical and practical understanding of human cognition, emotion, and behavior, as well as shaping our methodological approach to conducting psychological science. However, undertaking research in a world that merges the physical and virtual, also presents new and unique ethical challenges that are not addressed by current ethical guidelines such as the Belmont Report, the Ethical Principles of Psychologists and Code of Conduct, and the Association of Internet Researchers Internet Research Ethical Guidelines. We discuss the different domains of the metaverse relevant to psychological research and consider how three categories of ethical challenges (i.e., “respect for persons,” “beneficence,” and “justice”) may arise when conducting research in the metaverse. We also provide recommendations for addressing these challenges that include reconfiguring existing ethical guidelines as well as creating new ones. Together, these can inform and assist researchers and institutional review boards in making decisions about conducting ethically sound psychological science in the metaverse. |
A Framework for Language Technologies in Behavioral Research and Clinical Applications: Ethical Challenges, Implications, and Solutions
| Pengarang | : | Diaz-Asper, Catherine; Hauglid, Mathias K.; Chandler, Chelsea; Cohen, Alex S.; Foltz, Peter W.; Elvevåg, Brita |
| Nama Majalah/Jurnal | : | American Psychologist |
| Volume / Edisi | : | 79 (No. 1) |
| Halaman | : | 79-91 |
| Abstrak | : | Technological advances in the assessment and understanding of speech and language within the domains of automatic speech recognition, natural language processing, and machine learning present a remarkable opportunity for psychologists to learn more about human thought and communication, evaluate a variety of clinical conditions, and predict cognitive and psychological states. These innovations can be leveraged to automate traditionally time-intensive assessment tasks (e.g., educational assessment), provide psychological information and care (e.g., chatbots), and when delivered remotely (e.g., by mobile phone or wearable sensors) promise underserved communities greater access to health care. Indeed, the automatic analysis of speech provides a wealth of information that can be used for patient care in a wide range of settings (e.g., mHealth applications) and for diverse purposes (e.g., behavioral and clinical research, medical tools that are implemented into practice) and patient types (e.g., numerous psychological disorders and in psychiatry and neurology). However, automation of speech analysis is a complex task that requires the integration of several different technologies within a large distributed process with numerous stakeholders. Many organizations have raised awareness about the need for robust systems for ensuring transparency, oversight, and regulation of technologies utilizing artificial intelligence. Since there is limited knowledge about the ethical and legal implications of these applications in psychological science, we provide a balanced view of both the optimism that is widely published on and also the challenges and risks of use, including discrimination and exacerbation of structural inequalities. |