As part of the development of an information base for subsequent policy initiatives, the National Institute of Justice sponsored a nationwide survey of police psychologists to learn more about the characteristics of officers who abuse force, the types of measures police psychologists recommend to control police violence and the role of police psychologists in preventing and identifying individual police officers at risk for use of excessive force. Police personnel divisions in 50 large cities were contacted for names and addresses of the police psychologists who provided services to their departments. Data were collected using a telephone interview protocol that included 61 questions. In this study, excessive force was defined as a violation of a police department's use-of-force policy by an incumbent officer that was serious enough to warrant a referral to the police psychologist. Background information collected on respondents included years with the department, years as a police psychologist, if the position was salaried or consultant, and how often the psychologist met with the police chief. A battery of questions pertaining to screening was asked, including whether the psychologist performed pre-employment psychological screening and what methods were used to identify job candidates with a propensity to use excessive force. Questions regarding monitoring procedures asked if and how police officer behavior was monitored and if incumbent officers were tested for propensity to use excessive force. Items concerning police training included which officers the psychologist trained, what types of training covering excessive force were conducted, and what modules should be included in training to reduce excessive force. Information about mental health services was elicited, with questions on whether the psychologist counseled officers charged with excessive force, what models were used, how the psychologist knew if the intervention had been successful, what factors limited the effectiveness of counseling police officers, characteristics of officers prone to use excessive force, how these officers are best identified, and who or what has the most influence on these officers. General opinion questions asked about factors that increase excessive force behavior and what services could be utilized to reduce excessive force.

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools. The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011). The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels. References: Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5

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.

According to a March 2024 survey conducted in the United States, 32 percent of adults reported feeling that social media had neither a positive nor negative effect on their own mental health. Only seven percent of social media users said that online platforms had a very positive effect on their mental health, while 12 percent of users said it had a very negative impact. Furthermore, 22 percent of respondents said social media had a somewhat negative effect on their mental health. Is social media addictive? A 2023 survey of individuals between 11 and 59 years old in the United States found that over 73 percent of TikTok users agreed that the platform was addictive. Furthermore, nearly 27 percent of those surveyed reported experiencing negative psychological effects related to TikTok use. Users belonging to Generation Z were the most likely to say that TikTok is addictive, yet millennials felt the negative effects of using the app more so than Gen Z. In the U.S., it is also not uncommon for social media users to take breaks from using online platforms, and as of March 2024, over a third of adults in the country had done so. Following mental health-related content Although online users may be aware of the negative and addictive aspects of social media, it is also a useful tool for finding supportive content. In a global survey conducted in 2023, 32 percent of social media users followed therapists and mental health professionals on social media. Overall, 24 percent of respondents said that they followed people on social media if they had the same condition as they did. Between January 2020 and March 2023, British actress and model Cara Delevingne was the celebrity mental health activist with the highest growth in searches tying her name to the topic.

Mindful Emotion Regulation Impact

An Experimental Investigation

By [source]

About this dataset

In this dataset, we explore the impact of a mindful technique, the Safe Place induction on emotional regulation in a sample of 158 participants from Germany. The study aimed to examine whether the mindful detachment from negative emotions can lead to better emotion regulation. Data was contents responses collected through questionnaires administered before and after the intervention. It includes self-reported emotional states, as well as detachment from challenging emotion states and related behaviors which will help us understand how people respond when exposed to such techniques in order to be able to make more informed decisions about approaches that can be used for someone who experiences chronic or acute distressful episodes. The underlying connection between our emotions, thoughts and behavior is an important aspect since they are all interdependent and together they play a crucial role which was the focus on this study

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How to use the dataset

• Understand the variables: Before using the dataset, it is important to get an understanding of the different columns and variables that are included. The column names indicate which type of data is being measured and will help you identify what type of analysis you can perform on the data.

• Explore the data: After familiarizing yourself with the variables, start exploring the dataset to understand its structure and find out more about its content. Look for interesting trends or patterns in your data, as this may help guide future analysis steps or inspire additional questions about your research topic.

• Test hypotheses: Once you have a better understanding of your data, develop hypotheses about potential relationships between metrics from different columns in order to test them against each other statistically. This will help provide further evidence for or against your initial assumptions about emotional regulation techniques’ ability to impact moods and behaviors related to emotional states.

• Draw conclusions: After analyzing all relevant metrics from your dataset, summarize any significant findings from your research by drawing final conclusions based on these results and suggest possible applications they may have moving forward in terms of emotional regulation techniques’ effectiveness when it comes to managing moods and associated behaviors

Research Ideas

• Examining the effectiveness of different mindful techniques on emotion regulation.
• Analyzing changes in self-reported emotional states before and after the intervention.
• Investigating how emotion regulation techniques can be used to help individuals overcome difficult situations or circumstances

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

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Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

This dataset was used to investigate the brain mechanism underlying rumination state (Chen et al., 2020, NeuroImage). The data was shared through the R-fMRI Maps Project (RMP) and Psychological Science Data Bank.Investigators and AffiliationsXiao Chen, Ph. D. 1, 2, 3, 4, Chao-Gan Yan, Ph. D. 1, 2, 3, 41. CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China;2. International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China;3. Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China;4. Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China. AcknowledgmentsWe would like to thank the National Center for Protein Sciences at Peking University in Beijing, China, for assistance with data acquisition at PKU and Dr. Men Weiwei for his technical support during data collection. FundingNational Key R&D Program of China (2017YFC1309902);National Natural Science Foundation of China (81671774 and 81630031);13th Five-year Informatization Plan of Chinese Academy of Sciences (XXH13505);Key Research Program of the Chinese Academy of Sciences (ZDBS-SSW-JSC006);Beijing Nova Program of Science and Technology (Z191100001119104);Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences (Y9CX422005);China Postdoctoral Science Foundation (2019M660847). Publication Related to This DatasetThe following publication include the data shared in this data collection:Chen, X., Chen, N.X., Shen, Y.Q., Li, H.X., Li, L., Lu, B., Zhu, Z.C., Fan, Z., Yan, C.G. (2020). The subsystem mechanism of default mode network underlying rumination: A reproducible neuroimaging study. Neuroimage, 221, 117185, doi:10.1016/j.neuroimage.2020.117185. Sample SizeTotal: 41 (22 females; mean age = 22.7 ± 4.1 years).Exclusion criteria: Any MRI contraindications, current psychiatric or neurological disorders, clinical diagnosis of neurologic trauma, use of psychotropic medication and any history of substance or alcohol abuse. Scan procedures and ParametersMRI scanningSeveral days prior to scanning, participants were interviewed and briefed on the purpose of the study and the mental states to be induced in the scanner. Subjects also generated key words of 4 individual negative autobiographical events as the stimuli for the sad memory phase. We measured participants’ rumination tendency with the Ruminative Response Scale (RRS) (Nolen-Hoeksema and Morrow, 1991), which can be further divided into a more unconstructive subtype, brooding and a more adaptive subtype, reflection (Treynor, 2003). All participants completed identical fMRI tasks on 3 different MRI scanners (order was counter-balanced across participants). Time elapsed between 2 sequential visits were 22.0 ± 14.6 days. The fMRI session included 4 runs: resting state, sad memory, rumination state and distraction state. An 8-minute resting state came first as a baseline. Participants were prompted to look at a fixation cross on the screen, not to think anything in particular and stay awake. Then participants would recall negative autobiographical events prompted by individualized keywords from the prior interview. Participants were asked to recall as vividly as they could and imagine they were re-experiencing those negative events. In the rumination state, questions such as “Think: Analyze your personality to understand why you feel so depressed in the events you just remembered” were presented to help participants think about themselves, while in the distraction state, prompts like “Think: The layout of a typical classroom” were presented to help participants focus on an objective and concrete scene. All mental states (sad memory, rumination and distraction) except for the resting state contained four randomly sequentially presented stimuli (keywords or prompts). Each stimulus lasted for 2 minutes, and then was switched to the next without any inter-stimuli intervals (ISI), forming an 8-minute continuous mental state. The resting state and negative autobiographical events recall were sequenced first and second while the order of rumination and distraction states was counter-balanced across participants. Before the resting state and after each mental state, we assessed participants’ subjective affect with a scale (item score ranged from 1 = very unhappy to 9 = very happy). Thinking contents and the phenomenology during each mental state were assessed with a series of items which were derived from a factor analysis (Gorgolewski et al., 2014) regarding self-generated thoughts (item scores ranged from 1 = not at all to 9 = almost all). Image AcquisitionImages were acquired on 3 Tesla GE MR750 scanners at the Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences (henceforth IPCAS) and Peking University (henceforth PKUGE) with 8-channel head-coils. Another 3 Tesla SIEMENS PRISMA scanner (henceforth PKUSIEMENS) with an 8-channel head-coil in Peking University was also used. Before functional image acquisitions, all participants underwent a 3D T1-weighted scan first (IPCAS/PKUGE: 192 sagittal slices, TR = 6.7 ms, TE = 2.90 ms, slice thickness/gap = 1/0mm, in-plane resolution = 256 × 256, inversion time (IT) = 450ms, FOV = 256 × 256 mm, flip angle = 7º, average = 1; PKUSIEMENS: 192 sagittal slices, TR = 2530 ms, TE = 2.98 ms, slice thickness/gap = 1/0 mm, in-plane resolution = 256 × 224, inversion time (TI) = 1100 ms, FOV = 256 × 224 mm, flip angle = 7º, average=1). After T1 image acquisition, functional images were obtained for the resting state and all three mental states (sad memory, rumination and distraction) (IPCAS/PKUGE: 33 axial slices, TR = 2000 ms, TE = 30 ms, FA = 90º, thickness/gap = 3.5/0.6 mm, FOV = 220 × 220 mm, matrix = 64 × 64; PKUSIEMENS: 62 axial slices, TR = 2000 ms, TE = 30 ms, FA = 90º, thickness = 2 mm, multiband factor = 2, FOV = 224 × 224 mm). Code availabilityAnalysis codes and other behavioral data are openly shared at https://github.com/Chaogan-Yan/PaperScripts/tree/master/Chen_2020_NeuroImage. ReferencesGorgolewski, K.J., Lurie, D., Urchs, S., Kipping, J.A., Craddock, R.C., Milham, M.P., Margulies, D.S., Smallwood, J., 2014. A correspondence between individual differences in the brain's intrinsic functional architecture and the content and form of self-generated thoughts. PLoS One 9, e97176-e97176.Nolen-Hoeksema, S., Morrow, J., 1991. A Prospective Study of Depression and Posttraumatic Stress Symptoms After a Natural Disaster: The 1989 Loma Prieta Earthquake.Treynor, W., 2003. Rumination Reconsidered: A Psychometric Analysis.(Note: Part of the content of this post was adapted from the original NeuroImage paper)

The main results file are saved separately:- ASSR2.html: R output of the main analyses (N = 33)- ASSR2_subset.html: R output of the main analyses for the smaller sample (N = 25)FIGSHARE METADATACategories- Biological psychology- Neuroscience and physiological psychology- Sensory processes, perception, and performanceKeywords- crossmodal attention- electroencephalography (EEG)- early-filter theory- task difficulty- envelope following responseReferences- https://doi.org/10.17605/OSF.IO/6FHR8- https://github.com/stamnosslin/mn- https://doi.org/10.17045/sthlmuni.4981154.v3- https://biosemi.com/- https://www.python.org/- https://mne.tools/stable/index.html#- https://www.r-project.org/- https://rstudio.com/products/rstudio/GENERAL INFORMATION1. Title of Dataset:Open data: Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones2. Author Information A. Principal Investigator Contact Information Name: Stefan Wiens Institution: Department of Psychology, Stockholm University, Sweden Internet: https://www.su.se/profiles/swiens-1.184142 Email: sws@psychology.su.se B. Associate or Co-investigator Contact Information Name: Malina Szychowska Institution: Department of Psychology, Stockholm University, Sweden Internet: https://www.researchgate.net/profile/Malina_Szychowska Email: malina.szychowska@psychology.su.se3. Date of data collection: Subjects (N = 33) were tested between 2019-11-15 and 2020-03-12.4. Geographic location of data collection: Department of Psychology, Stockholm, Sweden5. Information about funding sources that supported the collection of the data:Swedish Research Council (Vetenskapsrådet) 2015-01181SHARING/ACCESS INFORMATION1. Licenses/restrictions placed on the data: CC BY 4.02. Links to publications that cite or use the data: Szychowska M., & Wiens S. (2020). Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones. Submitted manuscript.The study was preregistered:https://doi.org/10.17605/OSF.IO/6FHR83. Links to other publicly accessible locations of the data: N/A4. Links/relationships to ancillary data sets: N/A5. Was data derived from another source? No 6. Recommended citation for this dataset: Wiens, S., & Szychowska M. (2020). Open data: Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones. Stockholm: Stockholm University. https://doi.org/10.17045/sthlmuni.12582002DATA & FILE OVERVIEWFile List:The files contain the raw data, scripts, and results of main and supplementary analyses of an electroencephalography (EEG) study. Links to the hardware and software are provided under methodological information.ASSR2_experiment_scripts.zip: contains the Python files to run the experiment. ASSR2_rawdata.zip: contains raw datafiles for each subject- data_EEG: EEG data in bdf format (generated by Biosemi)- data_log: logfiles of the EEG session (generated by Python)ASSR2_EEG_scripts.zip: Python-MNE scripts to process the EEG dataASSR2_EEG_preprocessed_data.zip: EEG data in fif format after preprocessing with Python-MNE scriptsASSR2_R_scripts.zip: R scripts to analyze the data together with the main datafiles. The main files in the folder are: - ASSR2.html: R output of the main analyses- ASSR2_subset.html: R output of the main analyses but after excluding eight subjects who were recorded as pilots before preregistering the studyASSR2_results.zip: contains all figures and tables that are created by Python-MNE and R.METHODOLOGICAL INFORMATION1. Description of methods used for collection/generation of data:The auditory stimuli were amplitude-modulated tones with a carrier frequency (fc) of 500 Hz and modulation frequencies (fm) of 20.48 Hz, 40.96 Hz, or 81.92 Hz. The experiment was programmed in python: https://www.python.org/ and used extra functions from here: https://github.com/stamnosslin/mnThe EEG data were recorded with an Active Two BioSemi system (BioSemi, Amsterdam, Netherlands; www.biosemi.com) and saved in .bdf format.For more information, see linked publication.2. Methods for processing the data:We conducted frequency analyses and computed event-related potentials. See linked publication3. Instrument- or software-specific information needed to interpret the data:MNE-Python (Gramfort A., et al., 2013): https://mne.tools/stable/index.html#Rstudio used with R (R Core Team, 2020): https://rstudio.com/products/rstudio/Wiens, S. (2017). Aladins Bayes Factor in R (Version 3). https://www.doi.org/10.17045/sthlmuni.4981154.v34. Standards and calibration information, if appropriate:For information, see linked publication.5. Environmental/experimental conditions:For information, see linked publication.6. Describe any quality-assurance procedures performed on the data:For information, see linked publication.7. People involved with sample collection, processing, analysis and/or submission:- Data collection: Malina Szychowska with assistance from Jenny Arctaedius.- Data processing, analysis, and submission: Malina Szychowska and Stefan WiensDATA-SPECIFIC INFORMATION:All relevant information can be found in the MNE-Python and R scripts (in EEG_scripts and analysis_scripts folders) that process the raw data. For example, we added notes to explain what different variables mean.

CourseKata is a platform that creates and publishes a series of e-books for introductory statistics and data science classes that utilize demonstrated learning strategies to help students learn statistics and data science. The developers of CourseKata, Jim Stigler (UCLA) and Ji Son (Cal State Los Angeles) and their team, are cognitive psychologists interested in improving statistics learning by examining students' interactions with online interactive textbooks. Traditionally, much of the research in how students learn is done in a 1-hour lab or through small-scale interviews with students. CourseKata offers the opportunity to peek into the actions, responses, and choices of thousands of students as they are engaged in learning the interrelated concepts and skills of statistics and coding in R over many weeks or months in real classes.

1. items.csv (1335 X 19) Each row contains information about a particular question (although it does not provide the prompt). The item to which a question belongs is included. All items/questions are represented. Use this file to go deeper into particular questions that students encounter in the course.

Questions are grouped into items (item_id). An item can be one of three item_type 's: code, learnosity or learnosity-activity (the distinction between learnosity and learnosity-activity is not important). Code items are a single question and ask for R code as a response. (Responses can be seen in responses.csv.) Learnosity-activities and learnosity items are collections of one or more questions that can be of a variety of lrn_type's: ● association ● choicematrix ● clozeassociation ● formulaV2 ● imageclozeassociation ● mcq ● plaintext ● shorttext ● sortlist

Examples of these question types are provided at the end of this document.

The level of detail made available to you in the responses file depends on the lrn_type. For example, for multiple choice questions (mcq), you can find the options in the responses file in the columns labeled lrn_option_0 through lrn_option_11, and you can see the chosen option in the results variable.

Assessment Types In general, assessments, such as the items and questions included in CourseKata, can be used for two purposes. Formative assessments are meant to provide feedback to the student (and instructor), or to serve as a learning aid to help prompt students improve memory and deepen their understanding. Summative assessments are meant to provide a summary of a student's understanding, often for use in assigning a grade. For example, most midterms and final exams that you've taken are summative assessments.

The vast majority of items in CourseKata should be treated as formative assessments. The exceptions are the end-of-chapter Review questions, which can be thought of as summative. The mean number of correct answers for end-of-chapter review questions is provided within the checkpoints file. You might see that some pages have the word "Quiz" or "Exam" or "Midterm" in them. Results from these items and responses to them are not provided to us in this data set.

The Epidemiologic Catchment Area (ECA) program of research was initiated in response to the 1977 report of the President's Commission on Mental Health. The purpose was to collect data on the prevalence and incidence of mental disorders and on the use of and need for services by the mentally ill. The ECA Survey is the largest and most comprehensive survey of mental disorders ever conducted in the United States. The scope and complexity of the survey design were made possible because of the confluence of the recent standardization of psychiatric diagnostic criteria and the availability of advanced computer data processing systems. Independent research teams at five universities (Yale, Johns Hopkins, Washington University, Duke University, and University of California at Los Angeles), in collaboration with NIMH, conducted the studies with a core of common questions and sample characteristics. The sites were areas that had previously been designated as Community Mental Health Center catchment areas (New Haven, CN, Baltimore, MD, St. Louis, MO, Durham, NC, and Los Angeles, CA). The ECA encompassed a Household Survey and an Institutional Survey at each site, with two waves of personal interviews administered one year apart and a brief telephone interview in between. The structured psychiatric diagnostic interview used in the ECA was the NIMH Diagnostic Interview Schedule (DIS), version III (with the exception of the Yale Wave I survey, which used version II). Diagnostic and Statistical Manual of Mental Disorders, 3rd edition (DSM-III) diagnoses derived from the DIS include manic episode, major depressive episode, dysthymia, bipolar disorder, alcohol abuse or dependence, drug abuse or dependence, schizophrenia, schizophreniform, obsessive compulsive disorder, phobia, somatization, panic, antisocial personality, and anorexia nervosa. The DIS elicits diagnoses across the respondent's full life span and also indicates when symptoms appeared during the last year (within last two weeks, last month, last six months, and last full year). The DIS uses the Mini-Mental State Examination to screen, when respondents appear confused, for cognitive impairment and inability to complete the interview, and continuation by a proxy interview.

This dataset pertains to the first preliminary results of a longitudinal study relating to the impact of a new integrative therapeutic model for addictive disorders and psychological correlates. This first small study had the goal of understanding if the programme has a significant impact on certain psychological complaints that often appear in comorbidity with an addictive disorder, more specifically depressive symptomatology, suicide ideation and anxiety (state and trait as measured by STAI). MoCA was also used to screen for cognitive function at the beginning of treatment and was repeated at the end to evaluate possible changes. Preliminary results indicate a positive impact of the model in study regarding all the relevant variables. The sample is still small and further research needs to be made to confirm the results. Available are: the raw data, the statistical analyses output, information pertaining to the therapeutic model in study.

Burnout levels of residents in Middle-Eastern countries is further aggravated by sociopolitical instability. Around 70% of its population has experienced one or more of the following challenges: direct impact of war, a protracted economic crisis, an unstable sociopolitical situation and the catastrophic Beirut port explosion in August 2020. Medical residents in Lebanon have been at the forefront of these calamities. While burnout is an established threat to personal and professional wellbeing and performance, self-determination, measured by satisfaction of three psychological needs, has been suggested as a psychological state driving motivation and thus enhancing wellbeing at the workplace. Yet, the relationship between the satisfaction of the basic psychological needs specifically in medical residents, a population at particular risk for burnout, has not been studied. This study intends to explore prevalence of burnout and the relationship of its three determinants, depersonalization (DP), emotional exhaustion (EE) and lack of personal achievement (PA), and of BPN satisfaction or frustration, with the framework of the Self-Determination Theory (SDT) in healthcare. A cross sectional study among 110 medical residents served to measure basic psychological need satisfaction and frustration (BPNSFS), burnout (MBI), depression, and anxiety (PHQ-4). Residents were also asked about the subjective experience of the ongoing crises (COVID-19 pandemic, Beirut port explosion, and financial breakdown). Self-determination seems to be associated with more DP but with less EE and low PA, the latter being associated with more advanced mental distress. Further research should be done to understand this multifactorial phenomenon better and apply tools that enhance self-determination and improve residents’ resilience to buffer distress in future healthcare providers.

Background: Burnout is usually defined as a state of emotional, physical, and mental exhaustion that affects people in various professions (e.g. physicians, nurses, teachers). The consequences of burnout involve decreased motivation, productivity, and overall diminished well-being. The machine learning-based prediction of burnout has therefore become the focus of recent research. In this study, the aim was to detect burnout using machine learning and to identify its most important predictors in a sample of Hungarian high-school teachers. Methods: The final sample consisted of 1,576 high-school teachers (522 male), who completed a survey including various sociodemographic and health-related questions and psychological questionnaires. Specifically, depression, insomnia, internet habits (e.g. when and why one uses the internet) and problematic internet usage were among the most important predictors tested in this study. Supervised classification algorithms were trained to detect burnout assessed by two well-known burnout questionnaires. Feature selection was conducted using recursive feature elimination. Hyperparameters were tuned via grid search with 5-fold cross-validation. Due to class imbalance, class weights (i.e. cost-sensitive learning), downsampling and a hybrid method (SMOTE-ENN) were applied in separate analyses. The final model evaluation was carried out on a previously unseen holdout test sample. Results: Burnout was detected in 19.7% of the teachers included in the final dataset. The best predictive performance on the holdout test sample was achieved by random forest with class weigths (AUC = .811; balanced accuracy = .745, sensitivity = .765; specificity = .726). The best predictors of burnout were Beck’s Depression Inventory scores, Athen’s Insomnia Scale scores, subscales of the Problematic Internet Use Questionnaire and self-reported current health status. Conclusions: The performances of the algorithms were comparable with previous studies; however, it is important to note that we tested our models on previously unseen holdout samples suggesting higher levels of generalizability. Another remarkable finding is that besides depression and insomnia, other variables such as problematic internet use and time spent online also turned out to be important predictors of burnout.

The Dark Triad Dataset comprises psychological assessments and surveys designed to measure the three traits associated with the "dark triad": narcissism, Machiavellianism, and psychopathy. These traits are characterized by manipulative behavior, a lack of empathy, and a tendency towards self-promotion and exploitation of others.

File and column information:

M1 It's not wise to tell your secrets. M2 I like to use clever manipulation to get my way. M3 Whatever it takes, you must get the important people on your side. M4 Avoid direct conflict with others because they may be useful in the future. M5 It's wise to keep track of information that you can use against people later. M6 You should wait for the right time to get back at people. M7 There are things you should hide from other people because they don�t need to know. M8 Make sure your plans benefit you, not others. M9 Most people can be manipulated. N1 People see me as a natural leader. N2 I hate being the center of attention. N3 Many group activities tend to be dull without me.
N4 I know that I am special because everyone keeps telling me so. N5 I like to get acquainted with important people. N6 I feel embarrassed if someone compliments me. N7 I have been compared to famous people. N8 I am an average person. N9 I insist on getting the respect I deserve. P1 I like to get revenge on authorities. P2 I avoid dangerous situations. P3 Payback needs to be quick and nasty. P4 People often say I'm out of control. P5 It's true that I can be mean to others. P6 People who mess with me always regret it. P7 I have never gotten into trouble with the law. P8 I enjoy having sex with people I hardly know P9 I'll say anything to get what I want.

And these other values were calculated from technical information:

source How the user came to take the test. 1=from front page of website, 2=Google search, 3=other (based on HTTP referrer). country Using MaxMind GeoLite.

Number of Entries: 18,192

Number of Columns: 29

Columns and Data Types:

M1 to M9 (int64): These columns represent various metrics labeled from M1 to M9.

N1 to N9 (int64): These columns represent various metrics labeled from N1 to N9.

P1 to P9 (int64): These columns represent various metrics labeled from P1 to P9.

country (object): The country code (e.g., "GB" for Great Britain, "US" for United States).

source (int64):

The source of the data.

openpsychometrics.org

https://openpsychometrics.org/tests/SD3/

Conclusion:

The Dark Triad Dataset provides a valuable resource for understanding and studying the complex interplay of narcissism, Machiavellianism, and psychopathy in human behavior. It supports research efforts aimed at elucidating the psychological mechanisms underlying these traits and their implications for individuals and society at large.

This is an free online version of the Rosenberg Self Esteem Scale.

Validity This scale is the most widely used measure of self esteem for research purposes but it is NOT a diagnostic aid for any for any psychological issues of states. If you are worried that your self esteem may reflect poor mental health please consult your doctor. The scale has been used in more than one hundred research projects.

Because the concept of self esteem is one most people should be familar with, this test will proably not tell you anything you do not allready know. You should have a pretty good grasp of your results just by asking yourself the question, "do I have low self esteem?" The scale can however give you a better picture of your state in relation to other people. Your results will also include a little bit more about the relationship between self esteem and life outcomes.

Procedure The scale consists of ten statements that you could possibly apply to you that you must rate on how much you agree with each. The items should be answered quickly without overthinking, your first inclination is what you should put down.

Participation In addition to being offered for public education purposes, this survey is being used as part of a research project and your answers will be recorded. By starting this test you are agreeing to have any data you enter used for research.

Open Sex-Role Inventory This is an interactive personality test measuring masculinity and femininity (or gendered personality traits) modeled on the Bem Sex-Role Inventory.

Background In the 1970s Sandra Bem developed the Bem Sex-Role Inventory to challenge the view the masculinity and femininity were polar opposites and that a masculinity-femininity not matching your gender was a sign of poor mental health. Bem thought that it was possible to be both masculine and feminine at the same time and that this was the healthiest psychological state. The Open Sex Role Inventory was developed as open source, modernized measure of masculinity and femininity. The documentation of its development can be found here.

Test Instructions The test has 22 statements of opinion that you must rate on a seven point scale of how much you agree with each. It should take most people 4-6 minutes to complete.

Participation This test is provided for educational and entertainment use only. It should not be used as psychological advice of any kind and comes without any guarantee of accuracy or fitness for any particular purpose. Also, your responses may be recorded and anonymously used for research or otherwise distributed.

BackgroundA psychiatric interview is one of the important procedures in diagnosing psychiatric disorders. Through this interview, psychiatrists listen to the patient’s medical history and major complaints, check their emotional state, and obtain clues for clinical diagnosis. Although there have been attempts to diagnose a specific mental disorder from a short doctor-patient conversation, there has been no attempt to classify the patient’s emotional state based on the text scripts from a formal interview of more than 30 min and use it to diagnose depression. This study aimed to utilize the existing machine learning algorithm in diagnosing depression using the transcripts of one-on-one interviews between psychiatrists and depressed patients.MethodsSeventy-seven clinical patients [with depression (n = 60); without depression (n = 17)] with a prior psychiatric diagnosis history participated in this study. The study was conducted with 24 male and 53 female subjects with the mean age of 33.8 (± 3.0). Psychiatrists conducted a conversational interview with each patient that lasted at least 30 min. All interviews with the subjects between August 2021 and November 2022 were recorded and transcribed into text scripts, and a text emotion recognition module was used to indicate the subject’s representative emotions of each sentence. A machine learning algorithm discriminates patients with depression and those without depression based on text scripts.ResultsA machine learning model classified text scripts from depressive patients with non-depressive ones with an acceptable accuracy rate (AUC of 0.85). The distribution of emotions (surprise, fear, anger, love, sadness, disgust, neutral, and happiness) was significantly different between patients with depression and those without depression (p

AbstractObjectives:

Underpinned by pragmatism and symbolic interactionism, an inductive content analysis was conducted to assess participants’ experiences of driving under a variety of music conditions.

Background:

Numerous quantitative studies have addressed the positive and negative effects of music on drivers in both simulated and real-world environments. There has, however, been a conspicuous dearth of qualitative research to provide a deeper and more nuanced understanding of how drivers themselves think that music affects them.

Method:

Data collection took place over three simulated driving studies, each with different tasks/participants (Study 1 – n = 34, Study 2 – n = 46, and Study 3 – n = 27). Data were collected using four open-ended questions. The inductive content analysis was conducted by two members of the research team and a peer debriefing was conducted by a third to ensure the trustworthiness of the analysis.

Results:

Findings show that music can have a range of affective, behavioural and cognitive effects (both positive and negative), that are moderated by the driving environment (i.e., urban vs. highway) and aspects of the musical stimulus (i.e., inclusion/non-inclusion of lyrics, loudness, and tempo). Participants were mindful of the implications of in-vehicle music in regard to the safety–performance–pleasure trade off.

Conclusion:

The inductive content analysis suggested a perceived beneficial effect of music on the driving experience and consequent contribution to driving style and safety-related performance.

Application:

Younger drivers’ apparent reliance on music as a means by which to regulate emotion highlights a need for education of novice drivers.Detailed Description of Data FileThis Excel data file includes each participant’s responses to the 11 qualitative questions that were asked in the survey after a set of experimental trials. The 11 questions were: (1) Are you able to describe and differences among the six simulator trials that you completed over the last 90 minutes?; (2) How did each of the trials make you feel emotionally, in general, while you drove in the simulator (try to be specific)?; (3) Prior to this study, had you ever used music to influence your emotional state while driving in an urban environment and, if so, how exactly?; (4) Has listening to music during an urban driving simulation changed your perception of the experience in any way and, if so, how?; (5) Would listening to music during real urban driving make you likely to drive more safely in the future?; (6) What aspects of your emotions of behaviour during real urban driving is music likely to change?; (7) Would listening to a talk radio station or podcast during real urban driving make you likely to drive more safely in the future?; (8) What aspects of your emotions or behaviour during real urban driving would a talk radio station or podcase be likely to change?; (9) What sort of music would help you to drive more safely in a real urban environment (try to also give specific artists/albums/tracks)?; (10) Which trial did you think was most conducive to safe urban driving in the simulator and why?; and (11) Are there any other comments you would like to make in relation to the experimental protocol you have just completed?

BackgroundTaking part in a cancer clinical trial often represents a source of psychological distress and emotional activation among patients and their caregivers. Nowadays, social media platforms provide a space for these groups to freely express and share their emotional experiences.AimsWe aimed to reveal the most prevalent basic and complex emotions and sentiments in the posts of the patients and caregivers contemplating clinical trials on Reddit. Additionally, we aimed to categorize the types of users and posts.MethodsWith the use of keywords referring to clinical trials, we searched for public posts on the subreddit ‘cancer’. R studio v. 4.1.2 (2021-11-01) and NRC Emotion Lexicon was used for analysis. Following the theoretical framework of Plutchik’s wheel of emotions, the analysis included: 8 basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and 4 types of complex emotions (primary, secondary, tertiary, and opposite dyads). We utilized the package ‘PyPlutchik’ to visualize the emotion wheels in Python 3.10.5.ResultsA total of 241 posts were included in the final database. User types (129 patients, 112 caregivers) and post types (142 expressed shared experience, 77 expressed advice, and 85 conveyed both) were identified. Both positive (N = 2557, M = .68) and negative (N = 2154, M = .57) sentiments were high. The most prevalent basic emotions were: fear (N = 1702, M = .45), sadness (N = 1494, M = .40), trust (N = 1470, M = .44), and anticipation (N = 1376, M = .37). The prevalence of complex/dyadic emotions and their interpretation is further discussed.ConclusionIn this contribution, we identified and discussed prevalent emotions such as fear, sadness, optimism, hope, despair, and outrage that mirror the psychological state of users and affect the medical choices they make. The insights gained in our study contribute to the understanding of the barriers and reinforcers to participation in trials and can improve the ability of healthcare professionals to assist patients when confronted with this choice.

This paper deals with the problem of corruption, with a focus on both individual and country-specific institutional factors that may affect this problem. We analyse the determinants of the incidence of corruption as well as the tolerance of corruption. We used logit regressions that utilised data derived from Eurobarometer. The results strongly suggest gender, age, and education are important factors. We may say that anti-corruption policy ought to be targeted towards younger, less-educated, self-employed people with no children. On the other hand, a better-educated man in his early 30s seems to be a typical victim of corruption. The same is true for those having problems paying their expenses. Furthermore, contact with public officials appears to be one of the key issues, with Internet-based interactions with the government perhaps serving as the most effective solution to this problem. The rule of law, government effectiveness, and public accountability seem to be other factors that negatively correlate with the level of corruption within a country.

Epidemics are associated with increased burden of psychological distress. However, the role of boredom on mental health during epidemic periods has seldom been explored. This study attempted to examine the effect of state boredom on psychological outcomes, and the role of media use and meaning in life among the indirectly exposed Chinese adults in the initial phase of the COVID-19 outbreak. An online survey was administered to 917 Chinese adults on 28 January 2020 (1 week after the official declaration of person-to-person transmission of the coronavirus). Self-report questionnaires were used to assess state boredom, anxiety, depression, stress, media use and meaning in life. Moderated mediation analysis was conducted. Our results indicated that the effect of state boredom on anxiety and stress, but not depression, were mediated by media use and that sense of meaning in life modified this association. Meaning in life served as a risk factor, rather than a protective factor for the negative psychological outcomes when people experienced boredom. The association between boredom and media use was significant for high but not low meaning in life individuals. These findings demonstrated that boredom and media use were associated with an increased burden or psychological distress in the sample. It is important to pay attention to the possible negative impact of boredom and media use during COVID-19, and find more ways to cope with boredom, especially those with high presence of meaning in life.