Orientation: Managing mental health in the workplace has gained considerable attention in the field of industrial and organisational psychology, particularly as an essential element to managing employee well-being in South Africa. Research purpose: This study reviewed a decade of progress made in training industrial psychologists as workplace counsellors, as well as in identifying the best practices and future directions for addressing workplace mental health needs. Motivation for the study: Despite its growing recognition, significant gaps remain in training practitioners to ensure their preparedness in managing mental health in the workplace. Research approach/design and method: An integrative review was conducted on the author’s workplace counselling project, spanning from 2014 to 2025. A total of N = 23 studies met the inclusion criteria. Study characteristics and key findings were grouped into three domains – counselling models, trauma management frameworks and counsellor training methods. Main findings: Key findings showed progress in addressing training inadequacies, resulting in a revised edition of the framework for inspiring growth 2.0, which integrates mental health initiatives at individual, group and organisational intervention levels. Practical/managerial implications: A comprehensive workplace counselling training initiative incorporating multilevel approaches might enhance industrial psychologists’ competency and readiness in addressing workplace mental health challenges. Contribution/value-add: This review provides insight into developments in workplace counselling, including a review of past research. These practices guide future training of industrial psychologists in counselling practices.
Purpose: This article aims to analyze the implementation of Occupational Health and Safety (OHS) in organizational development at PT. Enggang Mill, a palm oil industry company. The primary focus of this study is to evaluate the company's policies, procedures, training, and efforts in fostering a safety culture, as well as to identify challenges in achieving a zero-incident environment.
Method: The methodology involves direct interviews with Mr. Adrian Syah, the Head of Safety at PT. Enggang Mill, and an analysis of the OHS policies and procedures in place. The collected data are analyzed qualitatively to understand the effectiveness and challenges in the execution of OHS programs within the factory setting.
Research Findings: The findings reveal that PT. Enggang Mill has implemented stringent safety policies and procedures, including regular training, provision of personal protective equipment (PPE), and the installation of warning signs throughout the work areas. However, the greatest challenge remains in cultivating a safety culture among employees, where further efforts are needed to enhance awareness and compliance with OHS procedures. The implications of this research emphasize the importance of management support and active employee participation in creating a safe and productive work environment. The novelty of this study lies in its comprehensive approach, combining an evaluation of safety policies with an analysis of safety culture within the company, providing insights into how safety culture can be a decisive factor in the success of OHS programs.
Tujuan Penelitian: Artikel ini bertujuan untuk menganalisis penerapan Kesehatan dan Keselamatan Kerja (K3) dalam pengembangan organisasi di PT. Enggang Mill, sebuah perusahaan industri kelapa sawit. Fokus utama dari studi ini adalah untuk mengevaluasi kebijakan, prosedur, pelatihan, dan upaya perusahaan dalam membangun budaya keselamatan, serta mengidentifikasi tantangan dalam mencapai lingkungan tanpa insiden.
Metode: Metode yang digunakan melibatkan wawancara langsung dengan Bapak Adrian Syah, Kepala Kesehatan dan Keselamatan di PT. Enggang Mill, serta analisis kebijakan dan prosedur K3 yang diterapkan. Data yang dikumpulkan dianalisis secara kualitatif untuk memahami efektivitas dan tantangan dalam pelaksanaan program K3 di lingkungan pabrik.
Temuan Penelitian: Temuan menunjukkan bahwa PT. Enggang Mill telah menerapkan kebijakan dan prosedur keselamatan yang ketat, termasuk pelatihan rutin, penyediaan alat pelindung diri (APD), dan pemasangan tanda peringatan di seluruh area kerja. Namun, tantangan terbesar tetap pada pembentukan budaya keselamatan di antara karyawan, di mana perlu ada upaya lebih lanjut untuk meningkatkan kesadaran dan kepatuhan terhadap prosedur K3. Implikasi dari penelitian ini menekankan pentingnya dukungan manajemen dan partisipasi aktif karyawan dalam menciPT.akan lingkungan kerja yang aman dan produktif. Kebaruan dari studi ini terletak pada pendekatannya yang komprehensif, menggabungkan evaluasi kebijakan keselamatan dengan analisis budaya keselamatan di dalam perusahaan, memberikan wawasan tentang bagaimana budaya keselamatan dapat menjadi faktor penentu dalam keberhasilan program K3.
Economics as a science, Management. Industrial management
Evidence about the associations between mental health and problematic social media use (PSMU) over time is mixed. While some studies have found mental health predicted PSMU over time, others found nonsignificant relationships. Therefore, the present study was aimed at investigating the impact of mental health (depression, anxiety, and wellbeing) on PSMU among young adults over time and investigating the potential mediating role of motives for social media use. The eMediate study participants (n=431, 49.7% female, age=22.6±1.8 years) who completed four waves of online questionnaires assessing social media use and mental health at 3-month intervals were included. Multilevel mediation analysis was used to examine the association between mental health and PSMU, and the possible mediating effect of motives for social media use. Depressive and anxiety symptoms and wellbeing significantly predicted PSMU over time, and social media use was motivated to cope with bad feelings, conform with others, be entertained, social interaction, escape from daily problems and stress, support seeking, and increase positive and decrease negative emotions. The escapism motive mediated the associations between symptoms of depression and anxiety and PSMU over time. The enhancing motive mediated the associations between depressive symptoms and wellbeing and PSMU over time. These findings provide insights into the motivational processes that may be driving the associations between mental health and PSMU, which could be targeted for intervention.
Large language models (LLMs) are rapidly being adopted across psychology, serving as research tools, experimental subjects, human simulators, and computational models of cognition. However, the application of human measurement tools to these systems can produce contradictory results, raising concerns that many findings are measurement phantoms--statistical artifacts rather than genuine psychological phenomena. In this Perspective, we argue that building a robust science of AI psychology requires integrating two of our field's foundational pillars: the principles of reliable measurement and the standards for sound causal inference. We present a dual-validity framework to guide this integration, which clarifies how the evidence needed to support a claim scales with its scientific ambition. Using an LLM to classify text may require only basic accuracy checks, whereas claiming it can simulate anxiety demands a far more rigorous validation process. Current practice systematically fails to meet these requirements, often treating statistical pattern matching as evidence of psychological phenomena. The same model output--endorsing "I am anxious"--requires different validation strategies depending on whether researchers claim to measure, characterize, simulate, or model psychological constructs. Moving forward requires developing computational analogues of psychological constructs and establishing clear, scalable standards of evidence rather than the uncritical application of human measurement tools.
Emma Franchino, Luciana Ciringione, Luisa Canal
et al.
Dealing with mathematics can induce significant anxiety, strongly affecting psychology students' academic performance and career prospects. This phenomenon is known as maths anxiety and several scales can measure it. Most scales were created in English and abbreviated versions were translated and validated among Italian populations (e.g. Abbreviated Maths Anxiety Scale). This study translated the 3-factor MAS-UK scale in Italian to produce a new tool, MAS-IT, validated specifically in a sample of Italian undergraduates enrolled in psychology or related BSc programmes. A sample of 324 Italian undergraduates completed the MAS-IT. The data were analysed using confirmatory Factor Analysis (CFA), testing the original MAS-UK 3-factor model. CFA results revealed that the original MAS-UK 3-factor model did not fit the Italian data. A subsequent Exploratory Graph Analysis (EGA) identified 4 distinct components/factors of maths anxiety detected by MAS-IT. The items relative to "Passive Observation maths anxiety" factor remained stable across the analyses, whereas "Evaluation maths anxiety" and "Everyday/Social maths anxiety" items showed a reduced or poor item stability. Quantitative findings indicated potential cultural or contextual differences in the expression of maths anxiety in today's psychology undergraduates, underlining the need for more appropriate tools to be used among psychology students.
We provide a critical review of a foundational article in neuroscience (Boyatzis & Jack, 2018) which set out to provide the neuroscientific foundations of Coaching to the PEA, a coaching model. Our critique questions the validity of the underpinning neuroscientific research; the appropriateness of selectively stimulating specified brain networks; the problematic positioning of the coach working with the brain; the rhetorical effects and paradigmatic challenges of integrating neuroscientific findings alongside other sources of knowledge; the risk of reductionism and of generalising findings from limited empirical research. Our critique questions how far neuroscience can be applied in coaching.
Special aspects of education, Industrial psychology
Nana Yaw Asabere, Isaac Ofori Asare, Gare Lawson
et al.
Both large and small information flows can have a significant impact on how consumers obtain trustworthy financial information, ultimately leading to an improvement in their daily lives when they interact dynamically with local geographic conditions. In economies that face both geographical and socioeconomic challenges, such as those in Africa, this kind of context is crucial. Large information flows provide significant issues such as big data challenges in the insurance sector, which calls for robust, demand-driven, and adaptive innovation solutions. In this paper, we present a geographic information system (GIS)–based location-aware recommender algorithm, called Geo-Insurance. Using some selected insurance companies in Accra, Ghana, as a point of view for location and customer data, our proposed Geo-Insurance solution addresses the big data challenges of customers finding the closest insurance companies with specific services through a web-based map created using a geodatabase file, ArcCatalog, and ArcGIS (among others). We conducted a series of benchmarking experiments. Our evaluation results show that Geo-Insurance performs better than other contemporary methods in terms of F-measure (F1), recall (R), precision (P), mean absolute error (MAE), and normalized MAE (NMAE).
Social media has become an increasingly vital tool for human resource management (HRM) in many parts of the globe. However, Asian societies have adopted social media for HRM at a lesser rate than Western cultures, which are more egalitarian and open, leading to greater comfort with using social media for professional interactions, even with superiors. This article provides a comprehensive literature review on the use of social media in HRM in Asian societies. The review analyzes 590 studies published between 2013 and 2023, following the PRISMA protocol for systematic reviews and using VOSviewer. The results indicate that the number of publications on this topic has fluctuated, with a notable increase in interest since 2015. The most prolific countries in terms of publications are India, China, Malaysia, Indonesia, Saudi Arabia, Pakistan, Taiwan, South Korea, Thailand, and the UAE. The study identifies significant research clusters and discusses the difficulties encountered when implementing social media technologies in HRM within an Asian context. These obstacles include cultural factors such as collectivism, power distance, and privacy concerns. The controversial findings regarding the distinction between excellent research and practical implementation demonstrate the need for additional research to understand better the potential benefits and challenges of incorporating social media into HRM practices in the region.
Fa Ji,1,2 Qilong Sun,3 Wei Han,3 Yansong Li,1 Xue Xia4,5 1School of Physical Education, Qingdao University, Qingdao, People’s Republic of China; 2Development Center for Water Sports, Qingdao University, Qingdao, People’s Republic of China; 3Liaocheng Infant Normal School, Liaocheng, People’s Republic of China; 4Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, People’s Republic of China; 5School of Social Development and Health Management, University of Health and Rehabilitation Sciences, Qingdao, People’s Republic of ChinaCorrespondence: Xue Xia, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), No. 1, Jiaozhou Road, Qingdao City, Shandong Province, People’s Republic of China, Email xiaxue@uor.edu.cn Yansong Li, School of Physical Education, Qingdao University, No. 308 Ningxia Road, Qingdao City, Shandong Province, People’s Republic of China, Email liyansong@qdu.edu.cnBackground: Problematic mobile phone use has become a pressing concern among adolescents due to its widespread prevalence and associated health risks. Physical exercise has been suggested as a potential intervention, but the psychological mechanisms underlying its effects remain unclear. This study explores how physical exercise impacts problematic mobile phone use through expression suppression, emotional problems (depression and anxiety), and resilience, offering actionable insights for intervention strategies.Methods: The study involved 2,032 Chinese adolescents who completed standardized self-report questionnaires assessing physical exercise, expression suppression, emotional problems, resilience, and problematic mobile phone use. Statistical analyses were conducted using a moderated serial mediation model.Results: Among the participants, 25.5% reported problematic mobile phone use, while 37.5% experienced both depression and anxiety. Physical exercise was negatively associated with problematic mobile phone use (β = − 0.195, p < 0.001) through its effects on expressive suppression, depression, and anxiety. Indirect effects mediated by expressive suppression and depression/anxiety accounted for 52.0% and 44.4% of the total effect, respectively. Additionally, resilience moderated the pathway linking expressive suppression to depression and anxiety (interaction effect for depression: β = − 0.080, 95% CI: − 0.111 to − 0.048; for anxiety: β = − 0.065, 95% CI: − 0.097 to − 0.033), with low resilience amplifying the negative emotional impacts of expressive suppression.Conclusion: Physical exercise can directly reduce problematic mobile phone use and indirectly alleviate its associated risks by improving emotion regulation and reducing emotional problems. Expressive suppression and depression/anxiety play significant mediating roles, while resilience moderates these pathways, highlighting its protective effect. By targeting both behavioral and psychological factors, interventions that combine physical activity promotion with resilience training show promise in addressing problematic mobile phone use and associated emotional issues in adolescents.Keywords: physical exercise, expressive suppression, depression/anxiety, resilience, problematic mobile phone use, adolescent
Kulumina Dash, Pratap Kumar Jena, Jigyansa Ipsita Pattnaik
et al.
Background:
The detrimental effects of air pollution on human health, particularly among vulnerable populations such as children, have raised concerns globally. While prior research has explored the association between air pollution and cognitive impairments, it is poorly studied in the Indian population.
Aim:
This study aims to specifically profile the cognitive deficits experienced by children residing in areas with high ambient particulate matter air pollution (PM10 and PM2.5) in Odisha.
Material and Methods:
A total of 30 children aged 6–8 years from Kalinga Nagar, Odisha were sampled, and their cognitive functions covering domains such as memory, attention, IQ, executive function, verbal skills, vocabulary, visuospatial ability, and processing speed and accuracy were assessed using the Malin’s Intelligence Scale for Indian Children (MISIC).
Results:
The mean full-scale IQ of the children was 84 as per MISIC, indicating that on average, the children’s IQ falls below the normal range. Specifically, the children showed lower performance in tests assessing attention, working memory, general knowledge acquisition, mathematical skills, vocabulary, and spatial reasoning.
Conclusion:
Six- to eight-year-old children residing in areas with high ambient particulate pollution exhibited lower cognitive abilities, including deficits in attention, working memory, mathematical skills, vocabulary, and visual-spatial processing.
Existing dialogue data augmentation (DA) techniques predominantly focus on augmenting utterance-level dialogues, which makes it difficult to take dialogue contextual information into account. The advent of large language models (LLMs) has simplified the implementation of multi-turn dialogues. Due to absence of professional understanding and knowledge, it remains challenging to deliver satisfactory performance in low-resource domain, like psychological dialogue dialogue. DA involves creating new training or prompting data based on the existing data, which help the model better understand and generate psychology-related responses. In this paper, we aim to address the issue of multi-turn dialogue data augmentation for boosted performance in the psychology domain. We propose a knowledge-driven progressive thought prompting method to guide LLM to generate multi-turn psychology-related dialogue. This method integrates a progressive thought generator, a psychology knowledge generator, and a multi-turn dialogue generator. The thought generated by the progressive thought generator serves as a prompt to prevent the generated dialogue from having significant semantic deviations, while the psychology knowledge generator produces psychological knowledge to serve as the dialogue history for the LLM, guiding the dialogue generator to create multi-turn psychological dialogue. To ensure the precision of multi-turn psychological dialogue generation by LLM, a meticulous professional evaluation is required. Extensive experiments conducted on three datasets related to psychological dialogue verify the effectiveness of the proposed method.
Memory behavior modeling is a core issue in cognitive psychology and education. Classical psychological theories typically use memory equations to describe memory behavior, which exhibits insufficient accuracy and controversy, while data-driven memory modeling methods often require large amounts of training data and lack interpretability. Knowledge-informed neural network models have shown excellent performance in fields like physics, but there have been few attempts in the domain of behavior modeling. This paper proposed a psychology theory informed neural networks for memory behavior modeling named PsyINN, where it constructs a framework that combines neural network with differentiating sparse regression, achieving joint optimization. Specifically, to address the controversies and ambiguity of descriptors in memory equations, a descriptor evolution method based on differentiating operators is proposed to achieve precise characterization of descriptors and the evolution of memory theoretical equations. Additionally, a buffering mechanism for the sparse regression and a multi-module alternating iterative optimization method are proposed, effectively mitigating gradient instability and local optima issues. On four large-scale real-world memory behavior datasets, the proposed method surpasses the state-of-the-art methods in prediction accuracy. Ablation study demonstrates the effectiveness of the proposed refinements, and application experiments showcase its potential in inspiring psychological research.
Wen-Bao Wang, Chich-Jen Shieh, Hamza Mohammed Ridha Al-Khafaji
et al.
The COVID-19 pandemic forced many organizations to move to telework and smart work (SW), and this practice is expected to continue even later in the postpandemic period. Hence, it is very important for managers and organizations to identify the motivating and deterrent factors in adopting smart work and plan to manage them. Therefore, the present study using an innovative methodology tried to identify and prioritize the factors influencing employee SW adoption. In the first stage, the conceptual model of the research was designed, inspired by the literature. In the next step, using structural equation modeling (SEM), antecedents whose effects on employee SW adoption were confirmed were identified. Finally, the output of the SEM model was considered as the input of the multilayer perceptron (MLP) model, which is an artificial neural network model, to determine the importance of each antecedent in the prediction of employee behavior. The present study provides quantitative empirical evidence that perceived value, institutional and technological support, perceived limited communication, and perceived cost are antecedents of employee SW adoption that are, respectively, important in predicting the behavioral intentions of employees in acceptance of SW. The findings of this study contribute to both the SW and the behavioral intention theory literature.
Graciela Guerrero, Daniel Avila, Fernando José Mateus da Silva
et al.
Background: Anxiety in university students can lead to poor academic performance and even dropout. The Adult Manifest Anxiety Scale (AMAS-C) is a validated measure designed to assess the level and nature of anxiety in college students. Objective: The aim of this study is to provide internet-based alternatives to the AMAS-C in the automated identification and prediction of anxiety in young university students. Two anxiety prediction methods, one based on facial emotion recognition and the other on text emotion recognition, are described and validated using the AMAS-C Test Anxiety, Lie and Total Anxiety scales as ground truth data. Methods: The first method analyses facial expressions, identifying the six basic emotions (anger, disgust, fear, happiness, sadness, surprise) and the neutral expression, while the students complete a technical skills test. The second method examines emotions in posts classified as positive, negative and neutral in the students' profile on the social network Facebook. Both approaches aim to predict the presence of anxiety. Results: Both methods achieved a high level of precision in predicting anxiety and proved to be effective in identifying anxiety disorders in relation to the AMAS-C validation tool. Text analysis-based prediction showed a slight advantage in terms of precision (86.84 %) in predicting anxiety compared to face analysis-based prediction (84.21 %). Conclusions: The applications developed can help educators, psychologists or relevant institutions to identify at an early stage those students who are likely to fail academically at university due to an anxiety disorder.
Sam Schirvar, Syed Mustafa Ali, Stephanie Dick
et al.
This article characterizes early research in the field of ‘human–computer interaction’ (HCI) by analysing the first decade of ‘user psychology’ research at Xerox's Palo Alto Research Center (PARC). PARC's Applied Information-Processing Psychology Project (AIP) provided an initial theoretical foundation for HCI in the early 1980s. Like researchers in artificial intelligence (AI), researchers at AIP drew from information-processing psychology. However, AIP researchers argued that their focus on human behaviour distinguished their research from AI and other fields allied with computer science. Previous scholarship has shown that United States computer engineers became concerned with ‘users’ as they sought to commercialize military-funded developments in interactive computing. This paper argues that the decision made by upper management in computerizing workplaces to shift some text production work from clerical workers to middle managers during the 1970s and 1980s led AIP to perceive ambiguities around gender and technical skill. This shaped the initial theoretical foundations that the research group offered to HCI – especially the group's conception of the ‘user’. Computer designers went from presenting word-processing programs as clerical machines for women workers to presenting them as tools for masculine thinking. AIP's research diverged from industrial engineering and AI in response to this transformation.
Language models have become an essential part of the burgeoning field of AI Psychology. I discuss 14 methodological considerations that can help design more robust, generalizable studies evaluating the cognitive abilities of language-based AI systems, as well as to accurately interpret the results of these studies.
We propose the concepts of philomatics and psychomatics as hybrid combinations of philosophy and psychology with mathematics. We explain four motivations for this combination which are fulfilling the desire of analytical philosophy, proposing science of philosophy, justifying mathematical algorithms by philosophy, and abstraction in both philosophy and mathematics. We enumerate various examples for philomatics and psychomatics, some of which are explained in more depth. The first example is the analysis of relation between the context principle, semantic holism, and the usage theory of meaning with the attention mechanism in mathematics. The other example is on the relations of Plato's theory of forms in philosophy with the holographic principle in string theory, object-oriented programming, and machine learning. Finally, the relation between Wittgenstein's family resemblance and clustering in mathematics is explained. This paper opens the door of research for combining philosophy and psychology with mathematics.
NLP datasets are richer than just input-output pairs; rather, they carry causal relations between the input and output variables. In this work, we take sentiment classification as an example and look into the causal relations between the review (X) and sentiment (Y). As psychology studies show that language can affect emotion, different psychological processes are evoked when a person first makes a rating and then self-rationalizes their feeling in a review (where the sentiment causes the review, i.e., Y -> X), versus first describes their experience, and weighs the pros and cons to give a final rating (where the review causes the sentiment, i.e., X -> Y ). Furthermore, it is also a completely different psychological process if an annotator infers the original rating of the user by theory of mind (ToM) (where the review causes the rating, i.e., X -ToM-> Y ). In this paper, we verbalize these three causal mechanisms of human psychological processes of sentiment classification into three different causal prompts, and study (1) how differently they perform, and (2) what nature of sentiment classification data leads to agreement or diversity in the model responses elicited by the prompts. We suggest future work raise awareness of different causal structures in NLP tasks. Our code and data are at https://github.com/cogito233/psych-causal-prompt
Department of Industrial Engineering and Innovation Sciences, University of Eindhoven, Eindhoven, Netherlands, Optentia Research Unit, North-West University, Vanderbijlpark, South Africa, Department of Human Resource Management, University of Twente, Enschede, Netherlands, Department of Social Psychology, Institut für Psychologie, Goethe University, Frankfurt am Main, Germany, WANT Research Team, Department of Developmental Psychology, Education, Social
Lindsay Y. Dhanani, Christopher W. Wiese, LeVonte’ Brooks
et al.
Abstract The country has been gripped by the events that have unfolded in the wake of the police killings of George Floyd and Breonna Taylor. In response to these new examples of long-standing police violence, there have been calls to substantially reimage policing to reduce the number of violent incidents that occur between officers and the public and to combat officers’ disproportionate use of force with Black Americans. In this article, we call on industrial-organizational (I-O) psychologists to leverage their expertise to help actuate meaningful change within law enforcement. To help guide our collective efforts as a field, we provide a review of the current state of affairs as they relate to recruitment, selection, training, performance management, occupational stress, and organizational culture in law enforcement and then offer recommendations for ways to change current practices to encourage more equitable and responsible policing. We also highlight areas in which further investigation is needed and urge I-O psychologists to invest in building the knowledge necessary to inform future practices. We hope this article can facilitate a discussion about how our field can contribute to achieving evidence-based and lasting change that benefits officers and the members of the communities they serve.