Dataset Summary

Psych-101 is a data set of natural language transcripts from human psychological experiments. It comprises trial-by-trial data from 160 psychological experiments and 60,092 participants, making 10,681,650 choices. Human choices are encapsuled in "<<" and ">>" tokens.

Paper: Centaur: a foundation model of human cognition Point of Contact: Marcel Binz

  Example Prompt

You will be presented with triplets of objects, which will be assigned to the keys D… See the full description on the dataset page: https://huggingface.co/datasets/marcelbinz/Psych-101.

This repository contains the raw dataset associated to the scientific article "Stable psychological traits predict psychological perceived stress to COVID-19 outbreak”, by L. Flesia, V. Fietta, B. Segatto, M. Monaro. Data are contained in the excel file and organized as follows:

- the entire dataset used by the authors to perform statistical analysis

- the training set used by the authors to train and validate ML models

- the test set used by the authors to test the ML models

The "Legend" file contains the description of each variable in the excel file.

The step by step instructions to replicate the results of ML classification models, which are reported in the paper, including two .arff files containing the training and test set od data that can be directly run in WEKA software 3.9.

The "COVID-19 QUESTIONNAIRE" file contains the English version of the questions administered to participants.

This dataset comprises both human expert and Large Language Model responses to queries about mental health

Please note: patient context and psychologist responses found within this dataset are all collected from Kaggle, from the NLP Mental Health Conversations repository.

The additional "LLM" column within this dataset has been generated by the MISTRAL-7B instruct v0.2 model, via the prompt:

You are a psychologist speaking to a patient. The patient will speak to you and you will then answer their query. [/INST] Okay. Go ahead, patient. I will answer you as a psychologist. [INST] Patient: QUERY_GOES_HERE Psychologist: [/INST]

This data was generated for, and analysed within the following study:

Bird, J.J., Wright, D., Sumich, A., and Lotfi, A., 2024, June. Generative AI in Psychological Therapy: Perspectives on Computational Linguistics and Large Language Models in Written Behaviour Monitoring. In Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments.

Psychological Testing Software Market Outlook

The global psychological testing software market size was estimated at approximately $1.2 billion in 2023 and is projected to reach around $3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5%. The market growth is primarily driven by the increasing need for mental health assessments, advancements in technology, and growing awareness of mental health issues worldwide. This burgeoning market is receiving heightened attention due to the rising prevalence of psychological disorders, the increasing adoption of digital health technologies, and the growing emphasis on mental health wellness across various sectors.

One of the primary growth factors for the psychological testing software market is the rising prevalence of mental health issues globally. With mental health conditions such as depression, anxiety, and stress disorders becoming more common, there is a heightened need for accurate and efficient diagnostic tools. Psychological testing software provides clinicians with reliable data to diagnose patients effectively, thereby improving treatment outcomes. Additionally, technological advancements in artificial intelligence and machine learning have enhanced the accuracy and effectiveness of these testing tools, making them indispensable in modern psychological practice.

Another significant driver for market growth is the increasing adoption of digital health technologies. The ongoing digital transformation in healthcare has paved the way for the integration of advanced software solutions in psychological assessments. Cloud-based platforms and mobile applications have made psychological testing more accessible and convenient, allowing both practitioners and patients to benefit from real-time data and remote consultations. This shift towards digital solutions is further fueled by the global pandemic, which has accelerated the adoption of telehealth services, including psychological assessments.

The growing awareness and destigmatization of mental health issues are also contributing to the market's expansion. Governments, non-profit organizations, and healthcare providers are increasingly focusing on mental health awareness campaigns, which are encouraging more individuals to seek psychological help. Educational institutions and corporate organizations are recognizing the importance of mental health and investing in psychological testing software to support their students and employees. These initiatives are creating a favorable environment for the growth of this market.

Assessment Software plays a pivotal role in the psychological testing landscape by providing a structured and efficient way to evaluate various mental health conditions. These software solutions are designed to streamline the assessment process, offering standardized tests that can be administered digitally. This not only enhances the accuracy of the assessments but also allows for a more comprehensive analysis of the results. As the demand for mental health services continues to rise, the adoption of assessment software is becoming increasingly crucial for healthcare providers, educational institutions, and corporate organizations. By leveraging these tools, practitioners can ensure that they are delivering the highest quality of care and support to their patients and clients.

Regionally, North America holds a significant share of the psychological testing software market, driven by the presence of advanced healthcare infrastructure, high adoption rates of digital health technologies, and a proactive approach to mental health. Europe follows closely, with an increasing focus on mental health policies and rising demand for psychological assessments. The Asia Pacific region is expected to witness substantial growth, attributed to the increasing awareness of mental health issues, rising healthcare expenditure, and advancements in healthcare technologies. Latin America and the Middle East & Africa, while currently smaller markets, are also anticipated to grow due to increasing healthcare initiatives and expanding digital health infrastructure.

Product Type Analysis

The psychological testing software market is segmented by product type into cognitive assessment software, personality assessment software, neuropsychological assessment software, and others. Each of these segments plays a crucial role in diagnosing and treating various psychological conditions, and their demand is influenced by different factors.&l

The study was conducted to evaluate the psychological effects of long-term administrative segregation (AS) on offenders, particularly those with mental illness. The longitudinal study examined five groups of inmates in the Colorado prison system over the course of one year: inmates in AS at the Colorado State Penitentiary (CSP) with mental illness, inmates in AS at the CSP without mental illness, inmates at risk of AS in the general population (GP) with mental illness, inmates at risk of AS in the GP without mental illness, and inmates at the San Carlos Correctional Facility, a facility for offenders with severe mental illness. Over the course of the study, researchers assessed each group of inmates using 14 psychological instruments, most of which were administered at three month intervals. Of the 14 psychological instruments, 12 were self-reports by inmates, 1 was filled out by mental health clinicians, and 1 was filled out by correctional staff.

We collect direct text data from the narrative of the people who faced psychological problem. Then, we make this dataset from the text. In this dataset there are 6 columns those are Age, Gender, Problem description, problem summary, problem category and problem psychological category.

Explore the growth potential of Market Research Intellect's Psychological Testing Softwares Market Report, valued at USD 3.2 billion in 2024, with a forecasted market size of USD 6.5 billion by 2033, growing at a CAGR of 8.5% from 2026 to 2033.

aAdjusted for the following variables at baseline: major depression, age, ethnicity, sex, household income, education level, generalized anxiety disorder, alcohol misuse, overall physical health, and overall mental health. Quality in relationships was rated on a four-point scale. Spouse/partner, family, and friends were included in the same multivariable model for analyses of participants with a spouse/partner and the just the latter two for analyses with all participants.

Psychological Testing and Assessment System Market Outlook

The global psychological testing and assessment system market size was valued at USD 5.7 billion in 2023 and is projected to reach USD 11.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.2% during the forecast period. The expansion of this market can be attributed to several key growth factors, including the rising prevalence of mental health disorders, the increasing awareness and acceptance of mental health issues, and advancements in technology that improve the accuracy and accessibility of psychological assessments.

One of the primary growth factors for the psychological testing and assessment system market is the global increase in mental health disorders. According to the World Health Organization, one in four people is affected by mental or neurological disorders at some point in their lives, underscoring the significant need for effective diagnostic tools. Psychological testing and assessment systems play a crucial role in early diagnosis and treatment planning, which is essential for improving patient outcomes. The growing recognition of mental health's impact on overall well-being is driving demand for these systems in both clinical and non-clinical settings.

Technological advancements represent another compelling growth factor. Innovations such as artificial intelligence (AI) and machine learning are being integrated into psychological testing and assessment systems, resulting in more accurate and reliable diagnostics. These technologies enable the analysis of large volumes of data, providing more comprehensive insights into an individual's mental health. Additionally, the advent of telehealth platforms has made psychological assessments more accessible, particularly in remote or underserved areas. This technological integration is expected to propel the market further in the coming years.

The increasing awareness and acceptance of mental health issues also contribute significantly to market growth. Societal stigmas surrounding mental health are gradually diminishing, thanks to widespread educational campaigns and advocacy. Governments and non-profit organizations are investing in initiatives to promote mental health awareness, leading to higher adoption rates of psychological assessment tools. This cultural shift is encouraging more individuals to seek help, thereby boosting the demand for reliable and effective assessment systems.

Higher Education Testing and Assessment is becoming increasingly significant in today's academic landscape. As educational institutions strive to enhance student outcomes and institutional effectiveness, the demand for comprehensive testing and assessment tools is on the rise. These tools are essential for evaluating student performance, identifying areas for improvement, and ensuring that educational standards are met. The integration of technology in higher education assessments has facilitated more efficient data collection and analysis, enabling educators to make informed decisions. Moreover, the focus on student-centered learning approaches has led to the development of adaptive assessments that cater to individual learning needs. This trend is expected to drive the growth of the higher education testing and assessment market, as institutions seek to improve educational quality and accountability.

From a regional perspective, North America is expected to dominate the market due to its well-established healthcare infrastructure and high awareness of mental health issues. Europe follows closely, driven by government initiatives and robust healthcare systems. The Asia Pacific region is anticipated to experience the fastest growth, fueled by increasing awareness and improving healthcare facilities. Latin America and the Middle East & Africa regions are also showing promising growth potential, albeit at a slower pace compared to other regions.

Component Analysis

The psychological testing and assessment system market is segmented by components, including software, hardware, and services. Each of these components plays a vital role in the functionality and efficiency of psychological assessment systems. The software segment is expected to hold the largest market share due to the increasing adoption of digital platforms for psychological assessments. Advanced software solutions offer a range of functionalities, including automated scoring, data storage, and real-time analytics, which enhance

Gilman-Adhered FilmClip Emotion Dataset (GAFED): Tailored Clips for Emotional Elicitation

Description:

Introducing the Gilman-Adhered FilmClip Emotion Dataset (GAFED) - a cutting-edge compilation of video clips curated explicitly based on the guidelines set by Gilman et al. (2017). This dataset is meticulously structured, leveraging both the realms of film and psychological research. The objective is clear: to induce specific emotional responses with utmost precision and reproducibility. Perfectly tuned for researchers, therapists, and educators, GAFED facilitates an in-depth exploration into the human emotional spectrum using the medium of film.

Dataset Highlights:

Gilman's Guidelines: GAFED's foundation is built upon the rigorous criteria and insights provided by Gilman et al., ensuring methodological accuracy and relevance in emotional elicitation.

Film Titles: Each selected film's title provides an immersive backdrop to the emotions sought to be evoked.

Emotion Label: A focused emotional response is designated for each clip, reinforcing the consistency in elicitation.

Clip Duration: Standardized duration of every clip ensures a uniform exposure, leading to consistent response measurements.

Curated with Precision: Every film clip in GAFED has been reviewed and handpicked, echoing Gilman et al.'s principles, thereby cementing their efficacy in triggering the intended emotion.

Emotion-Eliciting Video Clips within Dataset:

    Film
    Targeted Emotion
    Duration (seconds)




    The Lover
    Baseline
    43


    American History X
    Anger
    106


    Cry Freedom
    Sadness
    166


    Alive
    Happiness
    310


    Scream
    Fear
    395

The crowning feature of GAFED is its identification of "key moments". These crucial timestamps serve as a bridge between cinema and emotion, guiding researchers to intervals teeming with emotional potency.

Key Emotional Moments within Dataset:

    Film
    Targeted Emotion
    Key moment timestamps (seconds)




    American History X
    Anger
    36, 57, 68


    Cry Freedom
    Sadness
    112, 132, 154


    Alive
    Happiness
    227, 270, 289


    Scream
    Fear
    23, 42, 79, 226, 279, 299, 334

Based on: Gilman, T. L., et al. (2017). A film set for the elicitation of emotion in research. Behavior Research Methods, 49(6).

GAFED isn't merely a dataset; it's an amalgamation of cinema and psychology, encapsulating the vastness of human emotion. Tailored to perfection and adhering to Gilman et al.'s insights, it stands as a beacon for researchers exploring the depths of human emotion through film.

Note: BDI-II =  Beck Depression Inventory II, K6 =  Kessler's psychological distress scale, DAS =  Japanese version of the Dysfunctional Attitude Scale 24.* The number of participants was 193 in intervention group on Knowledge of cognitive restructuring and Efficacy of cognitive restructuring at baseline.

Amod/mental_health_counseling_conversations

  Dataset Summary

This dataset is a collection of real counselling question-and-answer pairs taken from two public mental-health platforms.It is intended for training and evaluating language models that provide safer, context-aware mental-health responses.

  Supported Tasks

Text generation and question-answering with an advice-giving focus.

  Languages

English (en)

  Dataset Structure





  Data… See the full description on the dataset page: https://huggingface.co/datasets/Amod/mental_health_counseling_conversations.

suayptalha/Psychological-Support dataset hosted on Hugging Face and contributed by the HF Datasets community

The COVID-19 pandemic, and the measures taken by governments around the world to contain it, had a huge impact on individuals' lives. According to a survey conducted in April 2021 in countries belonging to the G7 group, around ** percent of the respondents were negatively affected by the pandemic in terms of their psychological health. For ** percent of the participants in the survey, recovering from this experience will not be easy.

This study was conducted to examine the psychological reactions experienced by families of missing children and to evaluate families' utilization of and satisfaction with intervention services. To address issues of psychological consequences, the events occurring prior to child loss, during the experience of child loss, and after child recovery (if applicable) were studied from multiple perspectives within the family by interviewing parents, spouses, siblings, and, when possible, the missing child. A sample of 249 families with one or more missing children were followed with in-home interviews, in a time series measurement design. Three time periods were used: Time Series 1, within 45 days of disappearance, Time Series 2, at 4 months post-disappearance, and Time Series 3, at 8 months post-disappearance. Three groups of missing children and their families were studied: loss from alleged nonfamily abduction (stranger), loss by alleged family or parental abduction, and loss by alleged runaway. Cases were selected from four confidential sites in the United States. The files in this collection consist of data from detailed structured interviews (Parts 1-22) and selected quantitative nationally-normed measurement instruments (Parts 23-33). Structured interview items covered: (1) family of origin for parents of the missing child or children, (2) demographics of the current family with the missing child or children, (3) conditions in the family before the child's disappearance, (4) circumstances of the child's disappearance, (5) perception of the child's disappearance, (6) missing child search, (7) nonmissing child, concurrent family stress, (8) coping with the child's disappearance, (9) coping with a nonmissing child, concurrent family stress, (10) missing child recovery, if applicable, (11) recovered child reunification with family, if applicable, and (12) resource and assistance evaluation. With respect to intervention services, utilization of and satisfaction with these services were assessed in each of the following categories: law enforcement services, mental health services, missing child center services, within-family social support, and community social support. The quantitative instruments collected data on family members' stress levels and reactions to stress, using the Symptom Check List-90, Achenbach Child Behavior Check List, Family Inventory of Life Events, F-COPES, Frederick Trauma Reaction Index-Adult, and Frederick Trauma Reaction Index-Child.

A dataset containing basic conversations, mental health FAQ, classical therapy conversations, and general advice provided to people suffering from anxiety and depression.

This dataset can be used to train a model for a chatbot that can behave like a therapist in order to provide emotional support to people with anxiety & depression.

The dataset contains intents. An “intent” is the intention behind a user's message. For instance, If I were to say “I am sad” to the chatbot, the intent, in this case, would be “sad”. Depending upon the intent, there is a set of Patterns and Responses appropriate for the intent. Patterns are some examples of a user’s message which aligns with the intent while Responses are the replies that the chatbot provides in accordance with the intent. Various intents are defined and their patterns and responses are used as the model’s training data to identify a particular intent.

This data collection, conducted in a federal penitentiary and prison camp in Terre Haute, Indiana, between September 1986 and July 1988, was undertaken to examine the reliability and validity of psychological classification systems for adult male inmates. The classification systems tested were Warren's Interpersonal Maturity Level (I-level), Quay Adult Internal Management Systems (AIMS), Jesness Inventory, Megargee's MMPI-Based Prison Typology, and Hunt's Conceptual Level. The study sought to answer the following questions: (a) Which psychological classification systems or combination of systems could be used most effectively with adult populations? (b) What procedures (e.g., interview, paper-and-pencil test, staff assessment, or combination) would assure maximum efficiency without compromising psychometric precision? (c) What could the commonalities and differences among the systems reveal about the specific systems and about general classification issues pertinent to this population? and (d) How could the systems better portray the prison experience? The penitentiary was a low-maximum-security facility and the prison camp was a minimum-security one. A total of 179 penitentiary inmates and 190 camp inmates participated. The study employed both a pre-post and a correlational design. At intake, project staff members interviewed inmates, obtained social, demographic, and criminal history background data from administrative records and test scores, and then classified the inmates by means of an I-level diagnosis. Social and demographic data collected at intake included date of entry into the prison, age, race, marital status, number of dependents, education, recorded psychological diagnoses, occupation and social economic status, military service, evidence of problems in the military, ability to hold a job, and residential stability. Criminal history data provided include age at first nontraffic arrest, arrests and convictions, prison or jail sentences, alcohol or drug use, total number and kinds of charges for current offense, types of weapon and victims involved, co-offender involvement, victim-offender relationship, if the criminal activity required complex skills, type of conviction, and sentence length. T-scores for social maladjustment, immaturity, autism, alienation, manifest aggression, withdrawal, social anxiety, repression, and denial were also gathered via the Jesness Inventory and the MMPI. Interview data cover the inmates' interactions within the prison, their concerns about prison life, their primary difficulties and strategies for coping with them, evidence of guilt or empathy, orientation to the criminal label, relationships with family and friends, handling problems and affectivity, use of alcohol and drugs, and experiences with work and school. For the follow-up, the various types of assessment activities were periodically conducted for six months or until the inmate's release date, if the inmate was required to serve less than six months. Data collected at follow-up came from surveys of inmates, official reports of disciplinary infractions or victimizations, and prison staff assessments of inmates' prison adjustment and work performance. The follow-up surveys collected information on inmates' participation in treatment and educational programs, work absenteeism, health, victimization experiences and threats, awards, participation in aggressive, threatening, or other illegal activities, contact with family and friends, communication strategies, stress, sources of stress, and attitudes and beliefs about crime and imprisonment. Follow-up ratings by prison staff characterized the inmates on several clinical scales, according to each rater's global assessment of the interviewee. These characteristics included concern for others, role-taking abilities, assertiveness, inmate's relations with other inmates, authorities, and staff, verbal and physical aggressiveness, emotional control under stress, cooperativeness, need for supervision, response to supervision, maturity, behavior toward other inmates, and behavior toward staff.

Positive relationships have been established between childhood emotional neglect and psychological symptoms; however, the mechanisms underlying this relationship are not fully understood. Self-compassion acts as a protective factor against the development of these psychological symptoms. Cognitive-behavioral avoidance is considered a significant risk factor in the development of psychological symptoms. Therefore, the aim of this study is to examine the sequential mediating roles of self-compassion and cognitive-behavioral avoidance in the relationship between childhood emotional neglect and psychological symptoms. Data for the study were collected from university students aged 18-30 using the Childhood Trauma Questionnaire, Self-Compassion Scale, Cognitive-Behavioral Avoidance Scale, and Brief Symptom Inventory, both online and in-person. Descriptive statistics and correlation analyses were conducted using IBM SPSS v.4, while the entire mediation model was tested using the PROCESS MACRO (Model 6). According to the results of the correlation analysis, childhood emotional neglect is negatively correlated with self-compassion and positively correlated with cognitive-behavioral avoidance and psychological symptoms. Self-compassion is negatively correlated with cognitive-behavioral avoidance and psychological symptoms, while cognitive-behavioral avoidance is positively correlated with psychological symptoms. Finally, it was found that self-compassion and cognitive-behavioral avoidance play meaningful sequential mediating roles in the relationship between childhood emotional neglect and psychological symptoms. The findings are discussed within the relevant literature, and suggestions for future research and mental health professionals are provided.

Market Overview The global psychological assessment software market is projected to experience significant growth, with a CAGR of XX% during the forecast period of 2025-2033. This growth is attributed to several factors, including the increasing prevalence of mental health disorders, the growing demand for personalized and data-driven therapies, and the advancement of technology in the healthcare sector. Key drivers of the market include the rising awareness of mental health issues, the increasing adoption of digital health technologies, and the supportive government regulations. Segment Analysis The psychological assessment software market is segmented by type, application, and region. By type, the market is divided into personality testing, cognitive assessment, and others. By application, the market is segmented into the medical industry, public safety, education industry, research areas, and others. The North America region is expected to dominate the market throughout the forecast period due to factors such as the presence of a large number of established vendors, the high prevalence of mental health disorders, and the increasing adoption of advanced technologies.

Estimated means for intervention and control group, by risk status and results of the linear mixed models analysis.