This publication contains the official statistics about uses of the Mental Health Act ('the Act') in England during 2023-24. Under the Act, people with a mental disorder may be formally detained in hospital (or 'sectioned') in the interests of their own health or safety, or for the protection of other people. They can also be treated in the community but subject to recall to hospital for assessment and/or treatment under a Community Treatment Order (CTO). In 2016-17, the way we source and produce these statistics changed. Previously these statistics were produced from the KP90 aggregate data collection. They are now primarily produced from the Mental Health Services Data Set (MHSDS). The MHSDS provides a much richer data source for these statistics, allowing for new insights into uses of the Act. People may be detained in secure psychiatric hospitals, other NHS Trusts or at Independent Service Providers (ISPs). All organisations that detain people under the Act must be registered with the Care Quality Commission (CQC). In recent years, the number of detentions under the Act have been rising. An independent review has examined how the Act is used and has made recommendations for improving the Mental Health Act legislation. In responding to the review, the government said it would introduce a new Mental Health Bill to reform practice. This publication does not cover: 1. People in hospital voluntarily for mental health treatment, as they have not been detained under the Act (see the Mental Health Bulletin). 2. Uses of section 136 where the place of safety was a police station; these are published by the Home Office.

Latest monthly statistics on Learning Disabilities and Autism (LDA) patients from the Assuring Transformation (AT) collection and Mental Health Services Data Set (MHSDS). Data on inpatients with learning disabilities and/or autism are being collected both within the AT collection and MHSDS. There are differences in the inpatient figures between the AT and MHSDS data sets and work has been ongoing to better understand these. LDA data from MHSDS are experimental statistics, however, while impacts from the cyber incident are still present they will be considered to be management information. From April 2024, LDA MHSDS data has been collected under MHSDS version 6. From 1 July 2022, Integrated Care Boards were established within Integrated Care Systems and replaced Sustainability and Transformation Partnerships (STPs). Clinical Commissioning Groups have been replaced by sub-Integrated Care Boards. Data for the AT collection is now submitted by sub-Integrated Care Boards. This has resulted in some renaming within tables and the inclusion of a new Table 5.1b with a patient breakdown by submitting organisation. Patients by originating organisation and commissioning type are still available in Table 5.1a. Data in the tables are now presented by the current organisational structures. Old organisational structures have been mapped to new structures in any time series.

Hospital admissions for mental health conditions (0-17 years), per 100,000 population

This publication contains the official statistics about uses of the Mental Health Act(1) ('the Act') in England during 2020-21. Under the Act, people with a mental disorder may be formally detained in hospital (or 'sectioned') in the interests of their own health or safety, or for the protection of other people. They can also be treated in the community but subject to recall to hospital for assessment and/or treatment under a Community Treatment Order (CTO). In 2016-17, the way we source and produce these statistics changed. Previously these statistics were produced from the KP90 aggregate data collection. They are now primarily produced from the Mental Health Services Data Set (MHSDS). The MHSDS provides a much richer data source for these statistics, allowing for new insights into uses of the Act. However, some providers that make use of the Act are not yet submitting data to the MHSDS, or submitting incomplete data. Improvements in data quality have been made over the past year. NHS Digital is working with partners to ensure that all providers are submitting complete data and this publication includes guidance on interpreting these statistics. Please note: This publication covers the 2020-21 reporting year and, as such, it is likely the impact of COVID-19 may be evident as the national lockdown began on 23 March 2020. The time series data for people subject to detention does show a decrease in people subject to detention in March 2021 so the context of COVID-19 should be kept in mind when using and interpreting these statistics. Footnotes (1) The Mental Health Act 1983 as amended by the Mental Health Act 2007 and other legislation.

The National Mental Health Services Survey (N-MHSS) is an annual survey designed to collect statistical information on the numbers and characteristics of all known mental health treatment facilities within the 50 States, the District of Columbia, and the U.S. territories. In every other year, beginning in 2014, the survey also collects statistical information on the numbers and demographic characteristics of persons served in these treatment facilities as of a specified survey reference date. The N-MHSS is the only source of national and State-level data on the mental health service delivery system reported by both publicly-operated and privately-operated specialty mental health treatment facilities, including: public psychiatric hospitals; private psychiatric hospitals, non-federal general hospitals with separate psychiatric units; U.S. Department of Veterans Affairs medical centers; residential treatment centers for children; residential treatment centers for adults; outpatient or day treatment or partial hospitalization mental health facilities; and multi-setting (non-hospital) mental health facilities. The N-MHSS complements the information collected through SAMHSA's survey of substance abuse treatment facilities, the National Survey of Substance Abuse Treatment Services (N-SSATS). Treatment facility Information from the N-MHSS is used to populate the mental health component of SAMHSA's online Behavioral Health Treatment Services Locator.

An online resource for locating mental health treatment facilities and programs supported by the Substance Abuse and Mental Health Services Administration (SAMHSA). The Mental Health Treatment Locator section of the Behavioral Health Treatment Services Locator lists facilities providing mental health services to persons with mental illness. It includes: Public mental health facilities that are funded by their State mental health agency (SMHA) or other State agency or department Mental health treatment facilities administered by the Department of Veterans Affairs, Private for-profit and non-profit mental health facilities that are licensed by the State or accredited by a national accreditation organization. NOTE: The Mental Health Treatment Locator does not include facilities whose primary or only focus is the provision of services to persons with Mental Retardation (MR), Developmental Disability (DD), and Traumatic Brain Injuries (TBI). Facilities that provide treatment exclusively to persons with mental illness who are incarcerated. Mental health professionals in private practice (individual) or in a small group practice not licensed or certified as a mental health clinic or (community) mental health center. SAMHSA endeavors to keep the Locator current. All information in the Locator is updated annually based on facility responses to SAMHSA's National Mental Health Services Survey (N-MHSS). The most recent complete update includes data collected as of April 30, 2010 in the N-MHSS. New facilities are added monthly. Updates to facility names, addresses, telephone numbers and services are made weekly, if facilities inform SAMHSA of changes. For additional advice, you may call the Referral Helpline operated by SAMHSA's Center for Substance Abuse Treatment: 1-800-662-HELP (English & Español) 1-800-487-4889 (TTY)

This report presents findings from the 2018 National Mental Health Services Survey (N-MHSS), an annual census of all known facilities in the United States, both public and private, that provide mental health treatment services to people with mental illness. Planned and directed by the Center for Behavioral Health Statistics and Quality (CBHSQ) of the Substance Abuse and Mental Health Services Administration (SAMHSA), U.S. Department of Health and Human Services, the N-MHSS is designed to collect data on the location, characteristics, and utilization of organized mental health treatment services for facilities within the scope of the survey throughout the 50 states, the District of Columbia, Puerto Rico, and other jurisdictions.

On 24 June 2014 this page was edited and the National Statistics logo was removed. The HSCIC apologises for any confusion this may have caused. This statistical release makes available the most recent Mental Health Minimum Data Set (MHMDS) final monthly data (November 2013). This publication series replaces the Routine Quarterly MHMDS Reports, last published for the period Q4 2012-13, reflecting the change in the frequency of submissions. Further information about these changes and format of the monthly release can be found through the Resource links. This information will be of particular interest to organisations involved in delivering secondary mental health care for adults, as it presents timely information to support discussions between providers and commissioners of services. For patients, researchers, agencies and the wider public it aims to provide up to date information about the numbers of people using services, spending time in psychiatric hospitals and subject to the Mental Health Act (MHA). Some of these measures are currently experimental analysis. For this month's report we have added two new measures in the machine readable dataset - 16 year old bed days and 17 year old days. We've also added national, year to date figures on the number of people who had contact with secondary mental health services and the number of people who have spent at least one night as an inpatient in psychiatric hospital to the executive summary. In addition to the standard monthly outputs, this month's report includes a special feature focusing on our experimental analysis of uses of the Mental Health Act in adult mental health services from MHMDS

Participation in activities perceived to be meaningful is of importance in recovery processes among people with mental illness. This qualitative study explored experiences of participation in music and theater among people with long-term mental illness. Data were collected through in-depth interviews with 11 participants in a music and theater workshop carried out in a Norwegian mental health hospital context. Through a hermeneutical-phenomenological analysis, three central themes emerged: (a) engaging in the moment, (b) reclaiming everyday life, and (c) dreaming of a future. The findings indicate that participation in music and theater provided an opportunity to focus on enjoyable mundane activities and demonstrate how arts have the potential to bring meaning and more specifically small positive moments into participants’ lives. Despite seeming to be small in nature, these moments appeared to be able to add pleasure and meaning to the lives of those experiencing them. Consequently, there is a need to raise professionals’ awareness of these small positive moments of meaning, the power these experiences carry, and how to facilitate arenas which can provide such moments for people with long-term mental illness.

This indicator provides information about health professional shortage areas (HPSAs) for mental health services as determined by the federal Health Resources and Services Administration (HRSA). Each designated area includes multiple census tracts.HPSAs can be geographic areas, populations, or health care facilities that have been designated as having a shortage of health professionals. Geographic HPSAs have a shortage of providers for an entire population in a defined geographic area. Population HPSAs have a shortage of providers for a subpopulation in a defined geographic area, such as low-income populations, people experiencing homelessness, and migrant farmworker populations. In Los Angeles County, facility HPSAs include:•Federally Qualified Health Centers (FQHCs); •FQHC Look-A-Likes (LALs); •Indian Health Service, Tribal Health, and Urban Indian Health Organizations; •correctional facilities; • and some other facilities. For these indicators, we include HPSAs in Los Angeles County with statuses listed as “Designated” or “Proposed for Withdrawal” (but not withdrawn yet). Due to the nature of the designation process, a census tract may be designated as any combination of geographic and population HPSAs and three categories of care (i.e., primary, dental, and mental health care). Facility HPSAs may also cover multiple types of care.State Primary Care Offices submit applications to HRSA to designate certain areas within counties as HPSAs for primary care, dental, and mental health services. HRSA’s National Health Service Corps calculates HPSA scores to determine priorities for assignment of clinicians. The scores range from 0 to 25 for mental health, where higher scores indicate greater priority. All HPSA categories shared three scoring criteria: (1) population-to-provider ratio, (2) percent of population below 100% of the Federal Poverty Level, and (3) travel time to the nearest source of care outside the HPSA designation area. Each category also has additional criteria that go into the scores. Specifically, mental health HPSA scoring includes elderly ratio (percent of people over age 65), youth ratio (percent of people under age 18), alcohol abuse prevalence, and substance abuse prevalence. Note: if an area is not designated as an HPSA, it does not mean it is not underserved, only that an application has not been filed for the area and that an official designation has not been given.HPSA designations help distribute participating health care providers and resources to high-need communities.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This dataset shows the number of hospital admissions for influenza-like illness, pneumonia, or include ICD-10-CM code (U07.1) for 2019 novel coronavirus. Influenza-like illness is defined as a mention of either: fever and cough, fever and sore throat, fever and shortness of breath or difficulty breathing, or influenza. Patients whose ICD-10-CM code was subsequently assigned with only an ICD-10-CM code for influenza are excluded. Pneumonia is defined as mention or diagnosis of pneumonia. Baseline data represents the average number of people with COVID-19-like illness who are admitted to the hospital during this time of year based on historical counts. The average is based on the daily avg from the rolling same week (same day +/- 3 days) from the prior 3 years. Percent change data represents the change in count of people admitted compared to the previous day. Data sources include all hospital admissions from emergency department visits in NYC. Data are collected electronically and transmitted to the NYC Health Department hourly. This dataset is updated daily. All identifying health information is excluded from the dataset.

Context

The dataset contains the information about the mental health institutions around USA.

Content

I scraped the data from the official documents published by US government. I used the latest available document published for 2017. It's sorted according to states and the name of the institution and includes the address and phone numbers..

Acknowledgements

Inspiration

It would be useful to make some reasoning about the relationship between the number of institutions, location and the mental health deficiencies of the people living in the area.

THE IMPACTS OF WORKING REMOTELY AND IN AN OFFICE

Abstract: Working from home nowadays, particularly after COVID-19 hit the world, became the preferable choice for many employees because it gives flexibility and saves more time, according to them. However, many studies revealed that working from home caused a negative effect on many employees’ mental and physical health, such as isolation and back pain. The careless and unplanned way of living while working remotely, such as lack of socialization and equipment for a healthy home office, is the cause for that negative effect. In this paper, we explore the reasons that lead to the negative impact of working remotely on mental and physical health and investigate whether employees are aware of the negative and the positive effects of working either from home or in an office. Our investigation involved a questionnaire handed to hundred employees and revealed that the majority of them were aware of the negative and the positive impacts of working remotely and in an office and suggest, therefore, a mixed-mode of working to obtain the best advantages of both modes.

Keywords: COVID-19; working from home; working in an office; questionnaire; advantages; disadvantages; negative impact; positive impact; mental health; physical health; work experience

1. Introduction

Who would not like to wake up late and avoid the traffic every morning? I always had dreamed of that, and I guess you too. Working from home, which provides these advantages, has become the preferred choice for many employees and employers for the sake of getting more flexibility, increasing productivity, and saving time and money (Ipsen et al., 2021). I have noticed, especially during the COVID-19 pandemic, that many people switched willingly to work from home, expecting their life would totally improve. On the other hand, many people do not have the office work option. For instance, people work in the human resources, marketing, and customer service sectors (Iacurci, 2021). They work remotely until a hundred percent effective covid vaccine is developed. However, many studies, such as "Survey reveals the mental and physical health impacts of home working during Covid-19" by RSPH (2021), revealed that people who work from home are likely to suffer from mental and physical disorders.

In fact, the reason for the negative impact is not the work from home. Rather, it is the unmanaged lifestyle that comes with working from home. Of course, many other jobs still need people to be physically present, such as working in hospitals and beauty centers. However, Iacurci (2021) suggests that people will work remotely even after the pandemic finishes and the economy reopens. While many people are switching to work from home, and many others hoping so, it might be an opportunity for them to know the negative impact of working remotely, such as isolation and back pain, due to lack of socialization and equipment for a healthy home office. I am not willing to tell people what they should do in order to work healthily from home because this is not my study field. However, because I have experienced that negative impact, I will only give hints about the consequences, which could happen if they did not take care of themselves when working from home. Thus, this research investigated hundred people who have already worked before, regardless of gender identity, whether they are aware of the negative and the positive impacts of working from home in order to take care of themselves.

2. Literature review

Reviewing Worker's and Employees' Opinions in Working from Home

Before the COVID-19 pandemic, people could choose between working from home and in an office. However, many people are forced or got the opportunity to work from home to reduce the number of new daily infections during the pandemic. Thus, it was an opportunity for researchers to do research on a large number of people to figure out how working from home experience affected them. Also, after the pandemic is over, what would they prefer if they could choose between working remotely or being physically in an office.

In the study, "Six key advantages and disadvantages of working from home in Europe during COVID-19," Ipsen et al. (2021) investigated employees who have experience with working from home during the pandemic in 29 European countries. They used first the six key advantages and disadvantages approach, which involves the employees' opinions in working from home. Although the employees mentioned 16 disadvantages and 11 advantages, its results indicate that "the majority (55%) of employees were mostly positive about WFH" (p. 11). However, they assumed that maybe there are other circumstances that make the employees prefer working remotely over in an office. Hence, Ipsen et al. (2021) used the six factors approach, which involved the employe...

General information of mental health institutions.

The dataset contains income statement information for all licensed, comparable hospitals in the state of California. Kaiser hospitals, state mental hospitals, psychiatric health facilities, and hospitals with mainly long-term care patients are excluded. Deductions from Revenue, Net Patient Revenue, Net from Operations (Operating Revenue less Operating Expense), and Net Income for public hospitals has been adjusted for Disproportionate Share intergovernmental transfers for funding the Disproportionate Share Hospital Program. The program gets federal matching funds to pay supplemental payments to hospitals with a disproportionate share of uninsured, underinsured, and Medi-Cal patients. To link the OSHPD IDs with those from other Departments, like CDPH, please reference the "Licensed Facility Cross-Walk" Open Data table at https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk.

The Covid-19 pandemic has completely reshaped the lives of people around the world, including the lives of higher education students. Beyond serious health consequences for a proportion of those directly affected by the virus, the pandemic holds important implications for the life and work of higher education students, considerably affecting their physical and mental well-being. To capture how students perceived the first wave of the pandemic’s impact, one of the most comprehensive and large-scale online surveys across the world was conducted. Carried out between 5 May 2020 and 15 June 2020, the survey came at a time when most countries were experiencing the arduous lockdown restrictions. The online questionnaire was prepared in seven different languages (English, Italian, North Macedonian, Portuguese, Romanian, Spanish, Turkish) and covered various aspects of higher education students’ life, including socio-demographic and academic characteristics, academic life, infrastructure and skills for studying from home, social life, emotional life and life circumstances. Using the convenience sampling method, the online questionnaire was distributed to higher education students (aged 18 and over) and enrolled in a higher education institution. The final dataset consisted of 31,212 responses from 133 countries and 6 continents. The data may prove useful for researchers studying the pandemic’s impacts on various aspects of student life. Policymakers can utilize the data to determine the best solutions as they formulate policy recommendations and strategies to support students during this and any future pandemic.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. OP&R organizes and prepares for public health and medical emergencies through statewide partnerships with public - private and government agencies to coordinate an effective emergency response for the health and safety of all residents of Maryland. Last Updated: 10/06/2014 Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/Health/MD_LongTermCareAssistedLiving/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Excel tables in Russ & Eng. To compare patients, the MatchID parameter is used in each table - 1000-1060 are patients in the study sample, 4000-4060 are patients in the control group from a study in the USA (Chaudhary N, Luettich K, Peck MJ, Pierri E, Felber-Medlin L, Vuillaume G, Leroy P, Hoeng J, Peitsch MC. Physiological and biological characterization of smokers with and without COPD. F1000Res. 2017 Jun 13;6:877. doi: 10.12688/f1000research.11698.2. PMID: 29862011; PMCID: PMC5843826.) The files represent the following data DB.xlsx - General database; Index.xlsx - Database of hospital patients from which a sample of 60 people was recruited for the study. Files Diag 1-5.xlsx - Tables with concomitant diagnoses according to ICD-10 File Disabled.xlsx - Data table with analysis of disability among the studied group. Neurologist.xlsx - Concomitant diagnoses from examination by a neurologist. Therapist.xlsx - Concomitant diagnoses from examination by a therapist. SF-36_30_resp1-2 - Table used to evaluate the SF36 questionnaire.

The aim of the work: was to study the quality of life of patients in a psychiatric hospital, as well as to identify differences with a control group of healthy people. Methods: A cross-sectional observational study was conducted among inpatients of a psychiatric hospital using the SF-36 quality of life questionnaire. The sample included 60 patients of both sexes, aged 20 to 82 years. Some epidemiological, clinical, and anthropometric data for all subjects are presented. The results of the experimental group are compared with those of healthy individuals, based on retrospective published data. This was a pilot observational study of a cross-sectional nature. The data of the study group were collected in a short period from 01.01.24 to 31.05.24. The quality of life indicators of the study group were assessed during hospitalization using the SF-36 questionnaire. The results of the experimental group are compared with the results of observations of the control group based on retrospective published data from a study in the USA [10], where the open data presents the QOL indicators of a group of healthy subjects. A comparison is also given with the data of a healthy population in the Russian Federation obtained in a study (Simonov et al., 2013), and New Zealand (Frieling, Davis, Chiang, 2013) . The control data were chosen due to the availability of their open data, relative novelty, and also because the populations of these countries are predominantly Caucasian (demographic factor). In addition, the choice of these studies was due to their first appearance in the search for relevant information.

Readme file for ADAM-SDMH: A DAtaset from Manipal for Severity Detection in Tweets related to Mental Health Generated on 2021-02-15Recommended citation for the dataset:P. Surana, M. Yusuf and S. Singh, "Severity Classification of Mental Health-Related Tweets," 2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), 2021, pp. 336-341, DOI: 10.1109/DISCOVER52564.2021.9663651.******************************PROJECT INFORMATION******************************1. Title of dataset: Mental Health Dataset2. Author information:Praatibh Surana, Manipal Institute of Technology,Mirza Yusuf, Manipal Institute of Technology,Sanjay Singh, Manipal Institute of TechnologyPrincipal Investigators Name: Praatibh SuranaAddress: Manipal Institute of TechnologyEmail: praatibhsurana@gmail.comName: Mirza YusufAddress: Manipal Institute of TechnologyEmail: baig.yusuf.cr7@gmail.comCo-InvestigatorName: Sanjay SinghAddress: Manipal Institute of TechnologyEmail: sanjay.singh@manipal.edu3. Date of data collection: Jan 2021 - Feb 2021************************************DATA ACCESS INFORMATION************************************1. Licences/restrictions placed on access to the dataset: CC BY 4.02. Links to publications that use the data:URL: https://ieeexplore.ieee.org/document/9663651,DOI: 10.1109/DISCOVER52564.2021.96636513. Links to a third party or secondary data used in the project (for example, existing datasets, third-party datasets)Pennington, Jeffrey et al. “GloVe: Global Vectors for Word Representation.” EMNLP (2014).DOI: https://doi.org/10.3115/v1/d14-1162*****************************************METHODS OF DATA COLLECTION*****************************************1. Describe the methods for data collection and/or provide links to papers describing data collection methodsPaper DOI :Our research revolved around correctly classifying tweets based on their severity in a mental health context. An effort was also made to make the models detect sarcasm better, as this was something that many models in the past failed to do. Our dataset consists of tweets labeled as ‘0’, ‘1’, and '2' depending on their classes. The labeling rules followed are given in Table 1TABLE 1 - SEVERITY CLASSIFICATION CLASSES AND EXAMPLES-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Class | Rules | Example | |0 | Helping / suggestion for mental health awareness | Are you suffering from anxiety? Check out this page for therapy through Skype! | / positivity / informative | | / motivational | | / questions about mental health | | |1 | Sarcasm/rant/expression of annoyance | Today was so annoying. If my teacher would have called my name, I swear to God I would have killed myself | |2 | Case of slight disturbance | All I am is a burden. I don’t want to live anymore. | / strong indication of disturbance | | / user outright mentions depression | | / anxiety / suicide / self-harm |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------The following steps were performed for data collection:1) Tweets were extracted with the help of Twitter’s official API using hashtags such as #depression, #mentalhealth, #anxiety, #selfharm, #killmyself, and #kms from users.2) Around 40,000 tweets were extracted from Twitter between January and February 2021, out of which the final dataset comprised of 2460 tweets; 820 tweets were distributed equally amongst the three classes.3) Two authors manually annotated the dataset and cross-verified it to ensure accurate labeling.2. Data processing methods:A. Preprocessing1) Removal of numbers, URLs, usernames, and special characters: The first step after extraction of the tweets was ensuring that they were suitable for further use. The “preprocessor” uses the Python library to eliminate numbers, retweets, URLs, emojis, emoticons, and usernames, followed by duplicate tweets removal from the dataset.2) Stopword removal and expansion of standard abbreviations: We made use of Python’s “nltk” library for the removal of common stopwords such as “for,” “the,” “a,” etc. As our data is sourced from Twitter, lots of common internet abbreviations like “lol,” “kms,” “gn,”etc., were used. It was taken care of by converting these short forms to their corresponding complete forms. Lots of short forms like “wanna” for “want to” and “gonna” for “going to” were used. We ensured that these, too, were taken care of to the best of our abilities. 3) Removal of names, so that anonymity is maintained. Names of people, places, twitter handles anything that could compromise the anonymity has been removed, a token named as ‘[redacted]’ has been used in their place instead.*******************************SUMMARY OF DATA FILE*******************************Filename: MentalHealthTweets.csvShort description: This CSV File contains 2460 tweets annotated ‘0’, ‘1’ or ‘2’ based on the class they belong to.*******************************************************************DATA-SPECIFIC INFORMATION FOR NOTE: This section should be copied and pasted for each file*******************************************************************1. Number of variables: 22. Number of cases rows: 24613. Missing data codes: NA4. Variable listThe variables and their properties have been provided in Table 2TABLE 2 - VARIABLE DESCRIPTION TABLE----------------------------------------------------------------------Variable Name | Variable Description | Variable Type | |tweets | Cleaned up tweet | String | |label | Annotation for tweet | Integer----------------------------------------------------------------------

Association of clinical and sociodemographic characteristics with psychiatric diagnosis of people with severe mental illness not being recorded in general hospital records: Univariate and multivariable logistic regression.