This data was produced during the pilot phase of developing Arts-Based Dynamic Interpersonal Therapy (ADIT) during March 2019 - August 2020, we administered a survey to investigate referrers acceptability of ADIT (March - July 2020), based on their experience of the referral process and knowledge of ADIT. We also asked whether ADIT was acceptable in terms of perceived benefit to patients and whether ADIT was perceived to be a good use of NHS resources. Further to this a clinical researcher group ranked specific practice elements according to how much they were required to produce sustainable change for the patient. Lastly the pre/post raw data for GAD-7 and PHQ-9 are presented in their raw form.

Amod/mental_health_counseling_conversations

This dataset is a compilation of high-quality, real one-on-one mental health counseling conversations between individuals and licensed professionals. Each exchange is structured as a clear question–answer pair, making it directly suitable for fine-tuning or instruction-tuning language models that need to handle sensitive, empathetic, and contextually aware dialogue. Since its public release, it has been downloaded over 77,000 times (Aug… See the full description on the dataset page: https://huggingface.co/datasets/Amod/mental_health_counseling_conversations.

The table Publication Metadata is part of the dataset Counseling and Psychotherapy Transcripts: Volume I [full text data], available at https://stanford.redivis.com/datasets/4ew0-9qer43ndg. It contains 70567 rows across 43 variables.

Abstract

This collection contains plain text transcripts of therapy sessions, associated reference works and published narratives, memoirs, and other client narratives. Documents are sourced from Alexander Street Press' Counseling and Psychotherapy Transcripts: Volume I - Counseling and Psychotherapy Transcripts, Client Narratives, and Reference Works. The transcripts of therapy and counseling sessions illustrate a range of therapeutic techniques and practices. The pairing of these transcripts with associated reference works and published narratives, memoirs, and other client narratives enhances the collection's ability to provide insight into the experience of those undergoing therapy and living with mental illness. The dataset contains more than 2,000 session transcripts, 44,000 pages of client narratives, and 25,000 pages of secondary reference material.

Usage

For a complete list of materials, please see PSYC ESR with texts_QA completed by WS 1.7.21.xlsx (under Supporting files).

Under the **Files **tab:

• **Books/ **contains the plain text of books.

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• Transcripts/ contains the plain text of therapy transcripts.

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• **Raw Files/ **contains, where available, the original, scanned copies of books or therapy transcripts, usually as a pdf or series of jpgs.

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Under the **Tables **tab:

• Filter files by relevant features (e.g. School_of_Therapy, Symptoms) in the **Publication Metadata **table.
• Join the **Publication Metadata **extraction with the file indices to select files for export and analysis.

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This is the online supplement to a study to identify therapists' prototypes of four common mental disorders. Although earlier studies concluded that clinicians’ prototypes of patients with mental disorders can influence diagnostic decisions, it remains unclear how presumably more or less prototypical features were identified in these studies in the first place. As research on the content of therapists’ prototypes of mental disorders is very limited, the aim of the current study was to identify therapists’ prototypes of major depression, generalized anxiety disorder, borderline personality disorder, and bipolar disorder. Psychotherapists (N=69) filled out an online survey and answered questions on the most common thoughts, feelings, behaviors, appearance, life circumstances, age, and gender of a person with each disorder. Additionally, they rated the DSM-5 criteria according to how much they think about each criterion when thinking about a typical person with the respective disorder. The most frequently mentioned features of a typical person with each disorder are reported and positive and negative associations between features are visualized by means of network analyses . Besides some exceptions, therapists’ responses were mostly in line with the DSM-5 criteria and with frequencies of symptoms in patients with each disorder. Therapists’ prototypes might be helpful to make diagnostic decisions in typical situations but could lead to incorrectly diagnosing or overlooking a disorder in less typical situations. The results of the current study should be used to increase therapists’ awareness of prototypes and to emphasize the importance of accurate diagnosis, e.g. with structured interviews. Further research should investigate the influence of the prototypical features identified in the current study on therapists’ diagnostic decisions. Information on Research Data: The file "Kroeber et al 2023 Data.csv" includes the raw data and the file "Kroeber et al 2023 Codebook.csv" includes the corresponding codebook. To evaluate the open questions about thoughts, feelings, behaviors, appearance, and life circumstances of a typical person with BPD, GAD, or MD participants’ responses were categorized into features and interrater reliabilities for the features were calculated. The files "BPD categories 0 1.xlsx", "GAD categories 0 1.xlsx" and "MD categories 0 1.xlsx" include the lists of features coded with 0 = "feature was not mentioned by this participant" or 1 = "feature was mentioned by this participant". These files are used in the submitted R code. The csv-files include the same tables as the excel-files (e.g., "BPD appearance 0 1.csv") as well as the tables with participants' original responses in text form (e.g., "BPD appearance.csv") for long-term archiving. The files "BPD Reliability.zip", "GAD Reliability.zip" and "MD Reliability.zip" include separate excel-files for each feature for calculating interrater reliabilities. These files are used in the submitted R code. Additionally, all tables are included as csv-files for long-term archiving.

Dataset Card for "therapy-data-set-llama"

Created Dataset particularly focussed on conversations between a therapist and a patient which can be directly used for training of llama models. Raw Dataset is picked from Pandora Eg. Patient: Hi Therapist: Hello there. Tell me how are you feeling today? Patient: Is anyone there? Therapist: Hello there. Glad to see you're back. What's going on in your world right now? Patient: Good morning Therapist: Good morning. I hope you had a good… See the full description on the dataset page: https://huggingface.co/datasets/siddharthbulia/therapy-data-set-llama.

This is an auto-generated index table corresponding to a folder of files in this dataset with the same name. This table can be used to extract a subset of files based on their metadata, which can then be used for further analysis. You can view the contents of specific files by navigating to the "cells" tab and clicking on an individual file_kd.

Dataset Card for "phr_mental_health_dataset"

This dataset is a cleaned version of nart-100k-synthetic The data is generated synthetically using gpt3.5-turbo using this script. The dataset had a "sharegpt" style JSONL format, with each JSON having keys "human" and "gpt", having an equal number of both. The data was then cleaned, and the following changes were made The names "Alex" and "Charlie" were removed from the dataset, which can often come up in the conversation of fine-tuned… See the full description on the dataset page: https://huggingface.co/datasets/vibhorag101/phr_mental_therapy_dataset.

Dataset used for "Detecting Cognitive Distortions from Patient-Therapist Interactions" by Sagarika Shreevastava and Peter W. Foltz - 10.18653/v1/2021.clpsych-1.17

Therapist_responses.csv contains the questions asked by the patients and the respective response by a liscenced therapist. The ID_number given in this dataset can be used to match the annotated Patient's input to the therapist responses. The source of this data: https://www.kaggle.com/datasets/arnmaud/therapist-qa

Annotated_data.csv contains the 2530 annotated samples of the patient's input. This file contains the following columns: - ID_number: This can be used to match the respective therapist responses from the Therapist_responses.csv file. - Patient Question: This column has the Patient's questions that they posted for the therapists to respond. - Distorted part: The annotators were asked to select the sentences that indicated the presence of some distorted thinking. This column was left empty if no distortion was detected in a Patient's question. - Dominant Distortion: Due to the subjective nature of the task, it is not necessary that a single input will only contain a single distortion. The annotators were asked to select the most dominant distortion in the input for this column. If no distorion was detected then this column contains "No distortion". - Secondary distortion (Optional): This option was given to the annotators if they could not decide which the dominant distortion was among two types of cognitive distortions. If they could identify a single dominant distorion or if there was no distoriton detected, then this field was left empty.

Types of Distortions marked in the dataset:

1. All-or-nothing thinking This is a kind of polarized thinking. This involves looking at a situation as either black or white or thinking that there are only two possible outcomes to a situation. An example of such thinking is, "If I am not a complete success at my job; then I am a total failure."

2. Overgeneralization When major conclusions are drawn based on limited information, or some large group is said to have same behavior or property. For example: “one nurse was rude to me, this means all medical staff must be rude.” or “last time I was in the pool I almost drowned, I am a terrible swimmer and should not go into the water again”.

3. Mental filter A person engaging in filter (or “mental filtering) takes the negative details and magnifies those details while filtering out all positive aspects of a situation. This means: focusing on negatives and ignoring the positives. If signs of either of these are present, then it is marked as mental filter.

4. Should statements Should statements (“I should pick up after myself more”) appear as a list of ironclad rules about how a person should behave, this could be about the speaker themselves or other. It is NOT necessary that the word ‘should’ or it’s synonyms (ought to, must etc.) be present in the statements containing this distortion. For example: consider the statement – “I don’t have ups and downs like teenagers are supposed to; everything just seems kind of flat with a few dips”, this suggests that the person believes that a teenager should behave in a certain way and they are not conforming to that pattern, this makes it a should statement cognitive distortion.

5. Labeling Labeling is a cognitive distortion in which people reduce themselves or other people to a single characteristic or descriptor, like “I am a failure.” This can also be a positive descriptor such as “we were perfect”. Note that the tense in these does not always have to be present tense.

6. Personalization Personalizing or taking up the blame for a situation which is not directly related to the speaker. This could also be assigning the blame to someone who was not responsible for the situation that in reality involved many factors and was out of your/the person’s control. The first entry in the sample is a good example for this.

7. Magnification Blowing things way out of proportion. For example: “If I don’t pass this test, I would never be successful in my career”. The impact of the situation here is magnified. You exaggerate the importance of your problems and shortcomings, or you minimize the importance of your desirable qualities. Not to be confused with mental filter, you can think of it only as maximizing the importance or impact of a certain thing.

8. Emotional Reasoning Basically, this distortion can be summed up as - “If I feel that way, it must be true.” Whatever a person is feeling is believed to be true automatically and unconditionally. One of the most common representation of this is some variation of – ‘I feel like a failure so I must be a failure’. It does not always have to be about the speaker themselves, “I feel like he is not being honest with me, he must be hiding something” is also an example of emotional...

Introduction. Since its introduction in the DSM-5 and the ICD-11, the construct of personality functioning has received increased research interest. Recent studies have shown that psychotherapy contributes to an improvement in personality functioning. However, it remains unclear which factors predict an improvement. Methods. We used machine learning to filter out those variables that are relevant or irrelevant for the prediction of the improvement of personality functioning from all variables collected at the beginning of a therapy. We examined a sample of 648 completed psychotherapies from the Heidelberg Institute for Psychotherapy. Results. Overall, we found 4 groups of variables that were predictive of improvement in Personality Functioning: The patient's ability to enter relationships, his internalized relationship patterns, symptom severity, and how psychiatric the patient's disorder is. In addition, individual demographic factors and the patient's childhood memories proved to be predictive of the improvement in personality functioning. In contrast, the specific disorder pattern proved to be hardly predictive. Discussion. Our results thus reflect the experience of many therapists that for therapy to be successful, the external reality and inner world of experience should be the focus of treatment rather than the specific disorder. At the same time, our study with its many results provides a basis for future research. R, 4.31 The dataset only contains results and our RMarkdown script. Due to restrictions of the ethical commitee we are not even allowed to publish anonymised patient data.

Abstract of the Study: Introduction: Although personality functioning has a long psychodynamic tradition and has received renewed interest in psychotherapy research with the DSM-5 and ICD-11, almost nothing is known about its course and influencing factors following psychotherapy. Methods: In a sample of 1208 completed psychotherapies from the Heidelberg Institute for Psychotherapy, we examined changes in personality functioning in the 1-year follow-up. We then used machine learning to filter out the probable predictors from all 277 possible predictors for changes in personality functioning following psychotherapy. Results: On average, the improvement in personality functioning remained stable following psychotherapy. However, it was found that patients whose personality functioning worsened during psychotherapy improved again following psychotherapy. Patients who improved particularly well during psychotherapy worsened slightly following psychotherapy. In total, we found 14 predictors for improved personality functioning following psychotherapy. Discussion: All the influences found suggest that the change in psychotherapy is in part influenced by how well the patient succeeds in internalising the insights gained in psychotherapy or the therapist. If this does not succeed, the patient cannot compensate for the loss of co-regulation by the therapist at the end of therapy and some of the improvements in psychotherapy are lost. For example, we found that with fewer than 20 hours of psychotherapy, it must be assumed that the patient will worsen in personality functioning following psychotherapy. From 20 hours onwards, the improvement remains stable and from 95 hours onwards, a subsequent improvement can be expected. Content: This dataset contains all of our R code we used to calculate our results. We have also uploaded RMarkdown HTML, so our peers may double check our results. We further included all results which we could not publish in our manuscript. Due to restrictions by the ethical review board, we are not allowed to upload any kind of RData-File which contains the raw data or an imputed dataset.

Comprehensive dataset of 13,782 Reiki therapists in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The Home Infusion Therapy Providers dataset provides information on the Providers in Medicare who specialize in Home Infusion Therapy.

Data and R code used for the analysis of data for the publication: Coumoundouros et al., Cognitive behavioural therapy self-help intervention preferences among informal caregivers of adults with chronic kidney disease: an online cross-sectional survey. BMC Nephrology

Summary of study

An online cross-sectional survey for informal caregivers (e.g. family and friends) of people living with chronic kidney disease in the United Kingdom. Study aimed to examine informal caregivers' cognitive behavioural therapy self-help intervention preferences, and describe the caregiving situation (e.g. types of care activities) and informal caregiver's mental health (depression, anxiety and stress symptoms).

Participants were eligible to participate if they were at least 18 years old, lived in the United Kingdom, and provided unpaid care to someone living with chronic kidney disease who was at least 18 years old.

The online survey included questions regarding (1) informal caregiver's characteristics; (2) care recipient's characteristics; (3) intervention preferences (e.g. content, delivery format); and (4) informal caregiver's mental health. Informal caregiver's mental health was assessed using the 21 item Depression, Anxiety, and Stress Scale (DASS-21), which is composed of three subscales measuring depression, anxiety, and stress, respectively.

Sixty-five individuals participated in the survey.

See the published article for full study details.

Description of uploaded files

1. ENTWINE_ESR14_Kidney Carer Survey Data_FULL_2022-08-30: Excel file with the complete, raw survey data. Note: the first half of participant's postal codes was collected, however this data was removed from the uploaded dataset to ensure participant anonymity.

2. ENTWINE_ESR14_Kidney Carer Survey Data_Clean DASS-21 Data_2022-08-30: Excel file with cleaned data for the DASS-21 scale. Data cleaning involved imputation of missing data if participants were missing data for one item within a subscale of the DASS-21. Missing values were imputed by finding the mean of all other items within the relevant subscale.

3. ENTWINE_ESR14_Kidney Carer Survey_KEY_2022-08-30: Excel file with key linking item labels in uploaded datasets with the corresponding survey question.

4. R Code for Kidney Carer Survey_2022-08-30: R file of R code used to analyse survey data.

5. R code for Kidney Carer Survey_PDF_2022-08-30: PDF file of R code used to analyse survey data.

An observational, descriptive and cross-sectional study was carried out among Physiotherapists collegiates in Spain, who have worked for at least 3 months in direct care with patients. The aim of this study is to know the percentage of physiotherapists suffering from type two violence (sexual, physical or psychological/verbal violence in their clinical role) in Spain, as well as professional, clinical or personal variables that might be related to violence prevalence against physiotherapists by patients or their relatives/companions. In addition, the responses offered by the physical therapists and their perception of the results obtained have been consulted, as well as any personal consequences at health and work.

The study was emailed though Physiotherapists´ Colleges and/or disseminated through their webs from January to March 2022. Data were collected through an online form. After being informed of the objectives of the study, they voluntarily completed the data though an anonymous questionnaire. Data confidentiality was ensured through the use of Microsoft Forms software (Microsoft Office, Microsoft Corporation, USA) pursuant to an agreement with the University of A Coruña. The whole description of the methodology used has been published in: “Elaboración de un cuestionario sobre violencia(s) sufrida(s) por profesionales del ámbito de la Fisioterapia” [Developing a questionnaire about violence(s) suffered by professionals in the field of Physiotherapy] in Revista espanola de salud publica 97: e202306048 (2023-06-09). PMID: 37293946. ISSN (electronic): 2173-9110.

Additional related data collected that was not included in the current data package:2.942 respondents who agreed to participate and who had treated patients were obtained, but 9 answers were eliminated because of inconsistence in answers or because few cases of sex other were present, resulting 2.933 cases. In addition, some changes in presentation of data has been made: information on the autonomous community of origin and description of violent episodes by physiotherapists in their own words were eliminated. Age and clinical experience were collected in years but have been regrouped due to ethical restrictions; also, practice settings with low number of responses were included in the category “others” in order to protect anonymity.

Responses with inconsistencies (for example, between age and clinical experience) were excluded. Due to ethical restrictions, some questions have been eliminated or regrouped to guarantee anonymity.

Keywords: Workplace Violence, Prevalence, Physical Therapy, Risk factors (associated factors); Sexual harassment, physical abuse, job satisfaction

Information about funding sources or sponsorship that supported the collection of the data: This work has been sponsored by the General Council of Colleges of Physiotherapists in Spain.[Consejo General de Colegios de Fisioterapeutas de España]. Resources from Universidade da Coruña (University of A Coruna) have been employed. It has been also supported by MICINN grant PID2020-113578RB-I00, the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14). We wish to acknowledge the support received from the Centro de Investigación de Galicia “CITIC”, funded by Xunta de Galicia and the European Union.

Citation for and links to publications that cite or use the data: Boo-Mallo, T., Pérez-Caramés, A., Domínguez-Rodríguez, A., Oviedo-de-la-Fuente, M., Martínez-Rodríguez, A. Violence towards physical therapists in Spain, database from a national survey. Zenodo. https://doi.org/10.5281/zenodo.10599701

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

CBT-Bench Dataset

  Overview

CBT-Bench is a benchmark dataset designed to evaluate the proficiency of Large Language Models (LLMs) in assisting cognitive behavior therapy (CBT). The dataset is organized into three levels, each focusing on different key aspects of CBT, including basic knowledge recitation, cognitive model understanding, and therapeutic response generation. The goal is to assess how well LLMs can support various stages of professional mental health care… See the full description on the dataset page: https://huggingface.co/datasets/Psychotherapy-LLM/CBT-Bench.

This dataset comprise behavioural data recorded from 61 children diagnosed with Autism Spectrum Disorders (ASD). The data was collected during a large-scale evaluation of Robot Enhanced Therapy (RET). The dataset covers over 3000 therapy sessions and more than 300 hours of therapy. Half of the children interacted with the social robot NAO supervised by a therapist. The other half, constituting a control group, interacted directly with a therapist. Both groups followed the Applied Behavior Analysis (ABA) protocol. Each session was recorded with three RGB cameras and two RGBD (Kinect) cameras, providing detailed information of children's behaviour during therapy. This public release of the dataset noes not include video recordings or other personal information. Instead, it comprises body motion, head position and orientation, and eye gaze variables, all specified as 3D data in a joint frame of reference. In addition, metadata including participant age, gender, and autism diagnosis (ADOS) variables are included.

All data in this dataset is stored in JavaScript Object Notation (JSON) and can be downloaded here as DREAMdataset.zip. A much smaller archive comprising example data recorded from a single session is provided in DREAMdata-example.zip. The JSON format is specified in detail by the JSON Schema (dream.1.1.json) provided with this dataset.

JSON data can be read using standard libraries in most programming languages. Basic instructions on how to load and plot the data using Python and Jupyter are available in DREAMdata-documentation.zip attached with this dataset. Please refer to https://github.com/dream2020/data for more details.

The DREAM Dataset can be visualized using the DREAM Data Visualizer, an open source software available at https://github.com/dream2020/DREAM-data-visualizer. The DREAM RET System that was used for collecting this dataset is available at https://github.com/dream2020/DREAM.

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.

Background. The number of trials on psychotherapies for adult depression is very large and is quickly growing. Because of this large body of knowledge, it is important that the results of these studies are summarized and integrated in meta-analytic studies. More than a decade ago we developed a meta-analytic database of these trials, which was updated yearly through systematic literature searches. Recently, we developed a new version of this meta-analytic database, built on the systems and experience from our earlier database, but with completely new searches and improved methods. In this paper we will describe the methods and some first results of this database. Description. We conducted systematic literature searches in bibliographical databases (PubMed, Embase, PsycINFO, Cochrane Register of Controlled Trials to identify all trials on psychotherapy for adult depression (deadline January 1st, 2019). We excluded trials on maintenance and relapse prevention, dissertations, collaborative care, and studies not published in English, German, Spanish or Dutch. After reading 21,976 records (16,701 after exclusion of duplicates), we included 661 randomized trials. We distinguished the following categories of trials: Psychotherapy versus pharmacotherapy (65 studies), combined treatment versus pharmacotherapy alone (46), combined treatment versus psychotherapy alone (29), combined treatment versus psychotherapy plus placebo (18), psychotherapy versus control (335), psychotherapy versus another therapy (109), psychotherapy for inpatients (34), unguided self-help interventions (48), comparisons of different treatment formats (38), cognitive bias modification (14) and other comparisons (99). Over the years we have published several dozens of meta-analyses using this databases (including its previous versions). Conclusion. Psychotherapy for depression is definitely the best studied type of psychotherapy for any mental health problem. We hope that our database can be used as a resource for researchers who want to conduct systematic reviews and meta-analyses of subgroups of these studies.