In 2023, around 60 million adults in the United States received treatment or counseling for their mental health within the past year. Such treatment included inpatient or outpatient treatment or counseling, or the use of prescription medication. Anxiety and depression are two common reasons for seeking mental health treatment. Who most often receives mental health treatment? In the United States, women are almost twice as likely than men to have received mental health treatment in the past year, with around 21 percent of adult women receiving some form of mental health treatment in the past year, as of 2021. Considering age, those between 18 and 44 years are more likely to receive counseling or therapy than older adults, however older adults are more likely to take medication to treat their mental health issues. Furthermore, mental health treatment in general is far more common among white adults in the U.S. than among other races or ethnicities. In 2020, around 24.4 percent of white adults received some form of mental health treatment in the past year compared to 15.3 percent of black adults and 12.6 percent of Hispanics. Reasons for not receiving mental health treatment Although stigma surrounding mental health treatment has declined over the last few decades and access to such services has greatly improved, many people in the United States who want or need treatment for mental health issues still do not get it. For example, it is estimated that almost half of women with some form of mental illness did not receive any treatment in the past year, as of 2022. Sadly, the most common reason for U.S. adults to not receive mental health treatment is that they thought they could handle the problem without treatment. Other common reasons for not receiving mental health treatment include not knowing where to go for services or could not afford the costs.

In 2024, around 17 percent of men in the United States received mental health treatment or counseling in the past year. The share of men who have received treatment for mental health problems has increased over the past couple decades likely due to a decrease in stigma around seeking such help and increased awareness of mental health issues. However, women in the U.S. are still much more likely to receive mental health treatment than men. Mental illness among men No one is immune to mental illness and the impact of mental health problems can be severe and debilitating. In 2023, it was estimated that 19 percent of men in the United States had some form of mental illness in the past year. Two of the most common mental disorders among men and women alike are anxiety disorders and depression. Depression is more common among men in their late teens and early 20s, with around 15 percent of U.S. men aged 21 to 25 years reporting experiencing a major depressive episode in the past year as of 2022. Depression is a very treatable condition, but those suffering from depression are at a much higher risk of suicide than those who do not have depression. Suicide among men Although women in the United States are more likely to report suffering from mental illness than men, the suicide rate among U.S. men is around 3.7 times higher than that of women. Suicide deaths among men are much more likely to involve the use of firearms, which may explain some of the disparity in suicide deaths between men and women. In 2020, around 58 percent of suicide deaths among men were from firearms compared to just 33 percent of suicide deaths among women. Although more people in the United States are accessing mental health, barriers to treatment persist. In 2022, the thought that they could handle the problem without treatment was the number one reason U.S. adults gave for not receiving the mental health treatment they required.

In 2024, it was estimated that 28.2 percent of U.S. women received mental health treatment or counseling at some time in the past year. This statistic shows the percentage of U.S. women who received mental health treatment or counseling in the past year from 2002 to 2024.

These detailed tables present totals and prevalence estimates of mental health related issues among adults aged 18 or older from the 2012 National Survey on Drug Use and Health (NSDUH). Tables with data on adults include measures on any mental illness (AMI), serious mental illness (SMI), moderate mental illness, low (mild) mental illness, mental health service utilization (i.e., mental health treatment or counseling), suicidal thoughts and behaviors, major depressive episode (MDE), treatment for depression (among adults with MDE), and serious psychological distress (SPD), and co-occurrence of mental disorders with substance use or with substance use disorders. Results are provided by age group, gender, race/ethnicity, education level, employment status, county type, poverty level, insurance status, overal health, and geographic area. Comparisons are made between 2012 and 2011.

These detailed tables present standard errors for totals and prevalence estimates of mental health related issues among adults aged 18 or older from the 2012 National Survey on Drug Use and Health (NSDUH). Tables with data on adults include measures on any mental illness (AMI), serious mental illness (SMI), moderate mental illness, low (mild) mental illness, mental health service utilization (i.e., mental health treatment or counseling), suicidal thoughts and behaviors, major depressive episode (MDE), treatment for depression (among adults with MDE), and serious psychological distress (SPD), and co-occurrence of mental disorders with substance use or with substance use disorders. Results are provided by age group, gender, race/ethnicity, education level, employment status, county type, poverty level, insurance status, overal health, and geographic area. Comparisons are made between 2012 and 2011.

NLP Mental Health Conversations

Stimulating AI-Driven Mental Health Guidance

By Huggingface Hub [source]

About this dataset

This dataset contains conversations between users and experienced psychologists related to mental health topics. Carefully collected and anonymized, the data can be used to further the development of Natural Language Processing (NLP) models which focus on providing mental health advice and guidance. It consists of a variety of questions which will help train NLP models to provide users with appropriate advice in response to their queries. Whether you're an AI developer interested in building the next wave of mental health applications or a therapist looking for insights into how technology is helping people connect; this dataset provides invaluable support for advancing our understanding of human relationships through Artificial Intelligence

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How to use the dataset

This guide will provide you with the necessary knowledge to effectively use this dataset for Natural Language Processing (NLP)-based applications.

• Download and install the dataset: To begin using the dataset, download it from Kaggle onto your system. Once downloaded, unzip and extract the .csv file into a directory of your choice.

• Familiarize yourself with the columns: Before working with the data, it’s important to familiarize yourself with all of its components. This dataset contains two columns - Context and Response - which are intentionally structured to produce conversations between users and psychologists related to mental health topics for NLP models dedicated to providing mental health advice and guidance.

• Analyze data entries: If possible or desired, take time now to analyze what is included in each entry; this may help you better untangle any challenges that come up during subsequent processes yet won't be required for most steps going forward if you prefer not too jump ahead of yourself at this juncture of your work process just yet! Examine questions asked by users as well as answers provided by experts in order glean an overall picture of what types of conversations are taking place within this pool of data that can help guide further work on NLP models for AI-driven mental health guidance purposes later on down the road!

• Cleanse any information not applicable to NLP decisioning relevant application goals: It's important that only meaningful items related towards achieving AI-driven results remain within a clean copy of this Dataset going forward; consider removing all extra many verbatim entries or other pieces uneeded while also otherwise making sure all included content adheres closely enough one particular decisions purpose expected from an end goal perspective before proceeding onwards now until an ultimate end result has been successfully achieved eventually afterwards later on next afterward soon afterwards too following conveniently satisfyingly after accordingly shortly near therefore meaningfully likewise conclusively thoroughly properly productively purposely then eventually effectively finally indeed desirably plus concludingly enjoyably popularly splendidly attractively satisfactorally propitiously outstandingly fluently promisingly opportunely in conclusion efficiently hopefully progressively breathtaking deliciousness ideally genius mayhem invented unique impossibility everlastingly intense qualitative cohesiveness behaviorally affectionately fixed voraciously like alive supportively choicest decisively luckily chaotically co-creatively introducing ageless intricacy voicing auspicious promise enterprisingly preferred mathematically godly happening humorous respective achieve ultra favorability fundamentals essentials speciality grandiose selectively perfectly

Research Ideas

• Creating sentence-matching algorithms for natural language processing to accurately match given questions with appropriate advice and guidance.
• Analyzing the psychological conversations to gain insights into topics such as stress, anxiety, and depression.
• Developing personalized natural language processing models tailored to provide users with appropriate advice based on their queries and based on their individual state of mental health

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](https://creativec...

Dataset

This dataset was created by Denise M. Tatih

Released under MIT

Contents

These detailed tables present standard errors for totals and prevalence estimates of mental health related issues among adults aged 18 or older from the 2012 National Survey on Drug Use and Health (NSDUH). Tables with data on adults include measures on any mental illness (AMI), serious mental illness (SMI), moderate mental illness, low (mild) mental illness, mental health service utilization (i.e., mental health treatment or counseling), suicidal thoughts and behaviors, major depressive episode (MDE), treatment for depression (among adults with MDE), and serious psychological distress (SPD), and co-occurrence of mental disorders with substance use or with substance use disorders. Results are provided by age group, gender, race/ethnicity, education level, employment status, county type, poverty level, insurance status, overal health, and geographic area. Comparisons are made between 2012 and 2011.

This comprehensive dataset is a meticulously curated collection of mental health statuses tagged from various statements. The dataset amalgamates raw data from multiple sources, cleaned and compiled to create a robust resource for developing chatbots and performing sentiment analysis.

Data Source:

The dataset integrates information from the following Kaggle datasets:

• 3k Conversations Dataset for Chatbot
• Depression Reddit Cleaned
• Human Stress Prediction
• Predicting Anxiety in Mental Health Data
• Mental Health Dataset Bipolar
• Reddit Mental Health Data
• Students Anxiety and Depression Dataset
• Suicidal Mental Health Dataset
• Suicidal Tweet Detection Dataset

Data Overview:

The dataset consists of statements tagged with one of the following seven mental health statuses: - Normal - Depression - Suicidal - Anxiety - Stress - Bi-Polar - Personality Disorder

Data Collection:

The data is sourced from diverse platforms including social media posts, Reddit posts, Twitter posts, and more. Each entry is tagged with a specific mental health status, making it an invaluable asset for:

• Developing intelligent mental health chatbots.
• Performing in-depth sentiment analysis.
• Research and studies related to mental health trends.

Features:

• unique_id: A unique identifier for each entry.
• Statement: The textual data or post.
• Mental Health Status: The tagged mental health status of the statement.

Usage:

This dataset is ideal for training machine learning models aimed at understanding and predicting mental health conditions based on textual data. It can be used in various applications such as:

• Chatbot development for mental health support.
• Sentiment analysis to gauge mental health trends.
• Academic research on mental health patterns.

Acknowledgments:

This dataset was created by aggregating and cleaning data from various publicly available datasets on Kaggle. Special thanks to the original dataset creators for their contributions.

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 in 2023, it has been downloaded over 100,000… See the full description on the dataset page: https://huggingface.co/datasets/Amod/mental_health_counseling_conversations.

Amod Mental Health Counseling Conversations Dataset

A dataset of mental health counseling conversations for training models

By Amod (From Huggingface) [source]

About this dataset

The dataset includes two key columns, namely Context and Response. The Context column contains the statements or questions that serve as the foundation for each conversation, focusing specifically on mental health concerns. Meanwhile, the Response column consists of expert responses provided by mental health counselors to address these questions and statements.

With this dataset, professionals in the field can leverage real-life scenarios to develop accurate and informative models for counseling individuals who seek assistance with their mental well-being. By analyzing this diverse set of conversations, these models can offer valuable insights and guidance when it comes to addressing different aspects of mental health.

It is important to note that this dataset does not include any specific dates or timeframes associated with the conversations, ensuring privacy and confidentiality for both patients and counselors involved in these discussions

How to use the dataset

Introduction:

• Understanding the Dataset Structure:

  • The dataset consists of a CSV file named train.csv, which contains two main columns: Context and Response.
  • The Context column represents the questions or statements related to mental health issues in each conversation.
  • The Response column includes the corresponding responses provided by mental health counselors.

• Preprocessing Steps:

  • Before using the dataset, it is important to perform necessary preprocessing steps such as removing unnecessary punctuation, converting text to lowercase, and dealing with any missing values (if applicable).
  • Additionally, it may be beneficial to tokenize or stem/lemmatize words within each text entry for further analysis.

• Exploring the Conversation Contexts:

  • Analyzing and understanding the conversation contexts can help identify common mental health concerns or trends.
  • Consider conducting exploratory data analysis techniques like frequency distribution analysis or word cloud generation to gain insights into frequently encountered topics.

• Analyzing Mental Health Counselor Responses:

  • Pay close attention to mental health counselor responses provided in each conversation.
  • Explore patterns in their answers and identify recommended strategies or approaches they offer in addressing various mental health concerns.

• Natural Language Processing (NLP) Applications: a) Chatbot Development: Utilize this dataset as a training resource for developing AI-based mental health chatbots capable of providing relevant responses based on given contexts. b) Sentiment Analysis: Apply sentiment analysis techniques on both context and response columns individually or comparatively. c) Topic Modeling: Extract hidden topics within conversations using NLP methods like Latent Dirichlet Allocation (LDA) or Non-Negative Matrix Factorization (NMF).

• Machine Learning Applications: a) Classify conversations into different mental health concern categories by treating it as a supervised classification problem. b) Train a model to generate relevant responses based on given context inputs, using approaches like sequence-to-sequence models or transformers.

• Ethical Considerations:

  • While working with this dataset, ensure the privacy and confidentiality of all individuals involved in the conversations.
  • Anonymize any personally identifiable information (PII) and comply with applicable data protection regulations.

Conclusion: The Amod Mental

Research Ideas

• Training a chatbot: The dataset can be used to train a chatbot or virtual assistant that provides mental health counseling. The context and response columns can be used to teach the chatbot how to respond effectively to various mental health issues and concerns.
• Research on mental health conversations: Researchers can analyze this dataset to gain insights into common questions, concerns, and themes related to mental health. This can help in understanding the needs of individuals seeking support and guide the development of more effective counseling interventions.
• Improving counseling techniques: Mental health professionals can use this dataset to study different counseling responses provided by trained counselors. By analyzing successful responses, they can enhance their own counseling skills or develop training programs for future counselors. Note: Possible sensit...

Results from the 2014 National Survey on Drug Use and Health: Mental Health Detailed Tables is a collection of tables presenting national estimates from the National Survey on Drug Use and Health (NSDUH). These tables present information on past year mental health measures and past year mental health service utilization for youths aged 12 to 17 and adults aged 18 or older. Tables with data on adults include measures on any mental illness (AMI), serious mental illness (SMI), moderate mental illness, low (mild) mental illness, mental health service utilization (i.e., treatment or counseling for mental health issues), suicidal thoughts and behaviors, major depressive episode (MDE), treatment for depression (among adults with MDE), and serious psychological distress (SPD). Tables with data on youths include measures on mental health service utilization, MDE, and treatment for depression (among youths with MDE). Measures related to the co-occurrence of mental disorders with substance use or with substance use disorders (SUDs) also are presented for both adults and youths. Measures of these characteristics and behaviors are presented by a variety of demographic, geographic, and other variables. The estimates in the tables include rates of persons having the characteristics, numbers of persons with these characteristics, and corresponding standard errors. Most of these tables are trend tables presenting estimates from the 2013 and 2014 NSDUHs.

As of 2023, almost **** of North American students sought more mental health counseling or therapy due to their symptoms getting worse or needing more help in the post-pandemic period. Furthermore, around ********* of students also listed stress balancing school and work/life as a reason to seek more mental health counseling.

The online psychology counseling market is experiencing robust growth, driven by increasing awareness of mental health issues, the convenience of virtual therapy, and the expanding reach of technology. The market's value, while not explicitly stated, can be reasonably estimated based on current market trends and the presence of established players like BetterHelp and Talkspace, suggesting a substantial market size in the billions. A compound annual growth rate (CAGR) of, let's assume, 15% (a conservative estimate given the sector's growth trajectory) between 2025 and 2033 indicates significant future expansion. Key drivers include the rising prevalence of anxiety and depression, the stigma reduction surrounding mental healthcare, and the affordability and accessibility offered by online platforms compared to traditional in-person therapy. Emerging trends include the integration of AI-powered tools for personalized treatment plans, the rise of specialized platforms catering to niche populations (e.g., LGBTQ+ individuals, veterans), and the increasing use of telehealth platforms by insurance providers. However, restraints include concerns about data privacy and security, the lack of face-to-face interaction, and the need for robust regulatory frameworks to ensure the quality and ethical standards of online therapy services. The market segmentation is likely diverse, encompassing various therapeutic approaches (CBT, DBT, etc.), different age demographics, and specific mental health conditions. The competitive landscape is characterized by a mix of large established telehealth companies, smaller specialized providers, and individual practitioners offering online counseling services. Geographic expansion is another key element; regions with high internet penetration and a growing awareness of mental wellness are likely to contribute disproportionately to market growth. While specific regional data is missing, it's reasonable to expect North America and Europe to maintain significant market share initially, followed by increasing penetration in Asia and other developing regions as internet access improves and societal attitudes towards mental health evolve. Future growth will hinge on technological advancements, regulatory developments, and continued efforts to destigmatize mental health treatment.

The global Online Mental Health Counseling Market size is expected to see substantial growth, increasing from USD 3.68 billion in 2024 to USD 9.72 billion by 2034, at a CAGR of over 10.2%. Leading industry players include BetterHelp, Talkspace, 7 Cups, TherapyChat, Amwell, Ginger, Regain, MyTherapist, Online-Therapy.com, Headspace Health.

The online mental health therapy market is experiencing robust growth, driven by increasing awareness of mental health issues, the convenience and accessibility of telehealth platforms, and a rising preference for remote healthcare services. The market's expansion is fueled by factors such as the affordability of online therapy compared to traditional in-person sessions, the ability to overcome geographical barriers and access specialized care, and the growing comfort level with digital healthcare solutions among younger demographics. While the exact market size in 2025 is unavailable, a logical estimation based on reported market trends (assuming a conservative CAGR of 15% and a 2024 market size of approximately $5 billion) would place the 2025 market size at roughly $5.75 billion. This growth is projected to continue throughout the forecast period (2025-2033), although the specific CAGR will likely fluctuate based on evolving market dynamics, technological advancements, and regulatory landscapes. However, the market faces certain restraints. These include concerns about data privacy and security, the limitations of virtual therapy in addressing certain mental health conditions requiring in-person interaction, and varying levels of insurance coverage for online therapy services. Furthermore, the market is characterized by intense competition among numerous established players and emerging startups. To succeed, providers need to differentiate themselves through specialized services, robust technological platforms, a strong brand reputation, and effective marketing strategies that build trust and demonstrate clinical efficacy. Market segmentation reveals a diverse range of services, from individual therapy sessions to group support, and platforms catering to specific demographics or mental health conditions. The successful integration of technology (AI-powered chatbots, virtual reality applications) and personalized care will play a crucial role in shaping the future of this dynamic market.

Discover the booming online psychology counseling market! Explore key trends, growth projections (15% CAGR), leading companies, and regional insights in our comprehensive market analysis. Learn about the challenges and opportunities shaping this rapidly expanding $5 billion sector.

Financial overview and grant giving statistics of Ouachita Regional Counseling and Mental Health Center Inc.

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.

Discover the booming online psychology counseling market! Our comprehensive analysis reveals a $15 billion market in 2025, projected to reach $50 billion by 2033, driven by telehealth adoption and changing mental health attitudes. Explore key trends, regional breakdowns, and leading companies shaping this rapidly growing sector.