Context

Attention deficit hyperactivity disorder (ADHD) is a mental health disorder that can cause above-normal levels of hyperactive and impulsive behaviors. People with ADHD may also have trouble focusing their attention on a single task or sitting still for long periods of time. Both adults and children can have ADHD.

Content

All the Reddit posts (up to February 2021) and comments from https://www.reddit.com/r/ADHD/ and https://www.reddit.com/r/adhdwomen/, 2 major subreddits about ADHD.

Acknowledgements

To get all the data from the creation date of those 2 subreddits, I followed this tutorial:https://www.osrsbox.com/blog/2019/03/18/watercooler-scraping-an-entire-subreddit-2007scape/ and used PushShift API. However, there are some mistakes in the selftext of submissions, so I fed the id list into PRAW to get better data.

Inspiration

You can use this data to: - Entity recognition; - Sentiment analysis; - Help the ADHD community

This dataset contains posts from 28 subreddits (15 mental health support groups) from 2018-2020. We used this dataset to understand the impact of COVID-19 on mental health support groups from January to April, 2020 and included older timeframes to obtain baseline posts before COVID-19.

Please cite if you use this dataset:

Low, D. M., Rumker, L., Torous, J., Cecchi, G., Ghosh, S. S., & Talkar, T. (2020). Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study. Journal of medical Internet research, 22(10), e22635.

@article{low2020natural,
 title={Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study},
 author={Low, Daniel M and Rumker, Laurie and Torous, John and Cecchi, Guillermo and Ghosh, Satrajit S and Talkar, Tanya},
 journal={Journal of medical Internet research},
 volume={22},
 number={10},
 pages={e22635},
 year={2020},
 publisher={JMIR Publications Inc., Toronto, Canada}
}

License

This dataset is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://www.opendatacommons.org/licenses/pddl/1.0/

It was downloaded using pushshift API. Re-use of this data is subject to Reddit API terms.

Reddit Mental Health Dataset

Contains posts and text features for the following timeframes from 28 mental health and non-mental health subreddits:

• 15 specific mental health support groups (r/EDAnonymous, r/addiction, r/alcoholism, r/adhd, r/anxiety, r/autism, r/bipolarreddit, r/bpd, r/depression, r/healthanxiety, r/lonely, r/ptsd, r/schizophrenia, r/socialanxiety, and r/suicidewatch)
• 2 broad mental health subreddits (r/mentalhealth, r/COVID19_support)
• 11 non-mental health subreddits (r/conspiracy, r/divorce, r/fitness, r/guns, r/jokes, r/legaladvice, r/meditation, r/parenting, r/personalfinance, r/relationships, r/teaching).

filenames and corresponding timeframes:

• post: Jan 1 to April 20, 2020 (called "mid-pandemic" in manuscript; r/COVID19_support appears). Unique users: 320,364.
• pre: Dec 2018 to Dec 2019. A full year which provides more data for a baseline of Reddit posts. Unique users: 327,289.
• 2019: Jan 1 to April 20, 2019 (r/EDAnonymous appears). A control for seasonal fluctuations to match post data. Unique users: 282,560.
• 2018: Jan 1 to April 20, 2018. A control for seasonal fluctuations to match post data. Unique users: 177,089

Unique users across all time windows (pre and 2019 overlap): 826,961.

See manuscript Supplementary Materials (https://doi.org/10.31234/osf.io/xvwcy) for more information.

Note: if subsampling (e.g., to balance subreddits), we recommend bootstrapping analyses for unbiased results.

ObjectivePreventing suicide in US youth is of paramount concern, with rates increasing over 50% between 2007 and 2018. Statistical modeling using electronic health records may help identify at-risk youth before a suicide attempt. While electronic health records contain diagnostic information, which are known risk factors, they generally lack or poorly document social determinants (e.g., social support), which are also known risk factors. If statistical models are built incorporating not only diagnostic records, but also social determinants measures, additional at-risk youth may be identified before a suicide attempt.MethodsSuicide attempts were predicted in hospitalized patients, ages 10–24, from the State of Connecticut’s Hospital Inpatient Discharge Database (HIDD; N = 38943). Predictors included demographic information, diagnosis codes, and using a data fusion framework, social determinants features transferred or fused from an external source of survey data, The National Longitudinal Study of Adolescent to Adult Health (Add Health). Social determinant information for each HIDD patient was generated by averaging values from their most similar Add Health individuals (e.g., top 10), based upon matching shared features between datasets (e.g., Pearson’s r). Attempts were then modelled using an elastic net logistic regression with both HIDD features and fused Add Health features.ResultsThe model including fused social determinants outperformed the conventional model (AUC = 0.83 v. 0.82). Sensitivity and positive predictive values at 90 and 95% specificity were almost 10% higher when including fused features (e.g., sensitivity at 90% specificity = 0.48 v. 0.44). Among social determinants variables, the perception that their mother cares and being non-religious appeared particularly important to performance improvement.DiscussionThis proof-of-concept study showed that incorporating social determinants measures from an external survey database could improve prediction of youth suicide risk from clinical data using a data fusion framework. While social determinant data directly from patients might be ideal, estimating these characteristics via data fusion avoids the task of data collection, which is generally time-consuming, expensive, and suffers from non-compliance.

Ecuador Registered Activity Level Index: INA-R: Health & Social Work data was reported at 119.665 2002=100 in Apr 2018. This records an increase from the previous number of 114.133 2002=100 for Mar 2018. Ecuador Registered Activity Level Index: INA-R: Health & Social Work data is updated monthly, averaging 103.600 2002=100 from Jan 2003 (Median) to Apr 2018, with 184 observations. The data reached an all-time high of 172.780 2002=100 in Dec 2010 and a record low of 85.440 2002=100 in Oct 2015. Ecuador Registered Activity Level Index: INA-R: Health & Social Work data remains active status in CEIC and is reported by National Institute of Statistics and Census. The data is categorized under Global Database’s Ecuador – Table EC.A025: Registered Activity Level Index: INA-R.

Abstract

The main objective of a demographic household survey (DHS) is to provide estimates of a number of basic demographic and health variables. This is done through interviews with a scientifically selected probability sample that is chosen from a well-defined population.

The 2007 Nauru Demographic and Health Survey (2007 NDHS) was one of four pilot demographic and health surveys conducted in the Pacific under an Asian Development Bank ADB/ Secretariat of the Pacific Community (SPC) Regional DHS Pilot Project. The primary objective of this survey was to provide up-to-date information for policy-makers, planners, researchers and programme managers, for use in planning, implementing, monitoring and evaluating population and health programmes within the country. The survey was intended to provide key estimates of Nauru's demographics and health situation. The findings of the 2007 NDHS are very important in measuring the achievements of family planning and other health programmes. To ensure better understanding and use of these data, the results of this survey should be widely disseminated at different planning levels. Different dissemination techniques will be used to reach different segments of society.

The primary purpose of the 2007 NDHS was to furnish policy-makers and planners with detailed information on fertility, family planning, infant and child mortality, maternal and child health, nutrition, and knowledge of HIV and AIDS and other sexually transmitted infections.

NOTE: The only dissemination used was wide distribution of the report. A planned data use workshop was not undertaken. Hence there is some misconceptions and lack of awareness on the results obtained from the survey. The report is provided on the NBOS website free for download.

Geographic coverage

National Coverage - Districts

Analysis unit

• Households
• Children (0-14yrs)
• Individual women of reproductive age (15-49 yrs)
• Individual men of reproductive age (15yrs+)
• Facilities providing reproductive and child health services

Universe

The survey covered all household members (usual residents), - All children (aged 0-14 years) resident in the household - All women of reproductive age (15-49 years) resident in all household - All males (15yrs and above) in every second household (approx. 50%) resident in selected household

Results: The 2007 Nauru Demographic Health Survey (2007 NDHS) is a nationally representative survey of 655 eligible women (aged 15-49) and 392 eligible men (aged 15 and above).

Kind of data

Sample survey data [ssd]

Sampling procedure

IDG NOTES: Locate sampling documentation with SPC (Graeme Brown) and internal files. Add in this sections. Or second option dilute appendix A Sampling and extract key issues.

ESTIMATES OF SAMPLING ERRORS - Refer to Appendix A of final NDHS2007 report or; - External Resources - 2007 DHS- Appendix A and B Sampling (to be created separatedly by IDG progress ongoing)

Sampling deviation

IDG NOTES: Locate sampling documentation with Macro and internal files. Add in this section. Or second option dilute appendix B Sampling and extract key issues.

ESTIMATES OF SAMPLING ERRORS - Refer to Appendix B of final NDHS2007 report or;

• External Resources
  • 2007 DHS- Appendix A and B Sampling (to be created separatedly by IDG progress ongoing)

Extract:

In the 2007 NDHS Report of the survey results, sampling errors for selected variables have been presented in a tabular format. The sampling error tables should include:

.. Variable name

R: Value of the estimate; SE: Sampling error of the estimate; N: Unweighted number of cases on which the estimate is based; WN: Weighted number of cases; DEFT: Design effect value that compensates for the loss of precision that results from using cluster rather than simple random sampling; SE/R: Relative standard error (i.e. ratio of the sampling error to the value estimate); R-2SE: Lower limit of the 95% confidence interval; R+2SE: Upper limit of the 95% confidence interval (never >1.000 for a proportion).

Mode of data collection

Face-to-face [f2f]

Research instrument

DHS questionnaire for women cover the following sections:

• Background characteristics (age, education, religion, etc)
• Reproductive history
• Knowledge and use of contraception methods
• Antenatal care, delivery care and postnatal care
• Breastfeeding and infant feeding
• Immunization, child health and nutrition
• Marriage and recent sexual activity
• Fertility preferences
• Knowledge about HIV/AIDS and other sexually transmitted infections
• Husbands background and women's work

The men's questionnaire covers the same except for sections 4, 5, 6 which are not applicable to men.

It was also recognized that some countries have a need for special information that is not contained in the core questionnaire. Separate questionnaire modules were developed on a series of topics. These topics are optional and include:

• maternal mortality
• pill-taking behaviour
• sterilization experience
• children's education
• women's status
• domestic violence
• health expenditures
• consanguinity

The Papua New Guinea (PNG) questionnaire was proposed for Nauru to adapt as in comparison to the existing DHS model, this is not as lengthy and time-consuming. The PNG questionnaire also dealt with high incidence of alcohol and tobacco in Nauru. Questions on HIV/AIDS and STI knowledge were included in the men's questionnaire where it was not included in the PNG questionnaire.

Response rate

IDG NOTES: Locate response rate documentation with SPC (Graeme Brown) and internal files. Add in this sections.

Dataset from Barello S, Caruso R, Palamenghi L, Nania T, Dellafiore F, Bonetti L, Silenzi A, Marotta C, Graffigna G. Factors associated with emotional exhaustion in healthcare professionals involved in the COVID-19 pandemic: an application of the job demands-resources model. Int Arch Occup Environ Health. 2021 Nov;94(8):1751-1761. doi: 10.1007/s00420-021-01669-z. Epub 2021 Mar 3. PMID: 33660030; PMCID: PMC7928172.

Abstract

Purpose: The purpose of the present cross-sectional study is to investigate the role of perceived COVID-19-related organizational demands and threats in predicting emotional exhaustion, and the role of organizational support in reducing the negative influence of perceived COVID-19 work-related stressors on burnout. Moreover, the present study aims to add to the understanding of the role of personal resources in the Job Demands-Resources model (JD-R) by examining whether personal resources-such as the professionals' orientation towards patient engagement-may also strengthen the impact of job resources and mitigate the impact of job demands.

Methods: This cross-sectional study involved 532 healthcare professionals working during the COVID-19 pandemic in Italy. It adopted the Job-Demands-Resource Model to study the determinants of professional's burnout. An integrative model describing how increasing job demands experienced by this specific population are related to burnout and in particular to emotional exhaustion symptoms was developed.

Results: The results of the logistic regression models provided strong support for the proposed model, as both Job Demands and Resources are significant predictors (OR = 2.359 and 0.563 respectively, with p < 0.001). Moreover, healthcare professionals' orientation towards patient engagement appears as a significant moderator of this relationship, as it reduces Demands' effect (OR = 1.188) and increases Resources' effect (OR = 0.501).

Conclusions: These findings integrate previous findings on the JD-R Model and suggest the relevance of personal resources and of relational factors in affecting professionals' experience of burnout.

The baseline NCS, fielded from the fall of 1990 to the spring of 1992, was the first nationally representative mental health survey in the U.S. to use a fully structured research diagnostic interview to assess the prevalences and correlates of DSM-III-R disorders. The baseline NCS respondents were re-interviewed in 2001-02 (NCS-2) to study patterns and predictors of the course of mental and substance use disorders and to evaluate the effects of primary mental disorders in predicting the onset and course of secondary substance disorders. In conjunction with this, an NCS Replication survey (NCS-R) was carried out in a new national sample of 10,000 respondents. The goals of the NCS-R are to study trends in a wide range of variables assessed in the baseline NCS and to obtain more information about a number of topics either not covered in the baseline NCS or covered in less depth than we currently desire. A survey of 10,000 adolescents (NCS-A) was carried out in parallel with the NCS-R and NCS-2 surveys. The goal of NCS-A is to produce nationally representative data on the prevalences and correlates of mental disorders among youth. The NCS-R and NCS-A, finally, are being replicated in a number of countries around the world. Centralized cross-national analysis of these surveys is being carried out by the NCS data analysis team under the auspices of the World Health Organization (WHO) World Mental Health Survey Initiative. In order to provide an easily accessible database which can be updated and checked on a regular basis, we have created a public use file system containing all the documents from the NCS and NCS-R programs. These file systems can be accessed through the Internet and either downloaded onto a disk or printed. We will update the system on a regular basis to add newly completed paper abstracts and other documents. In addition, the NCS and NCS-R data can be accessed through ICPSR (Inter-university Consortium for Political and Social Research). Any updates to the data to correct coding or classification errors will be made available along with written documentation of the changes in ICPSR''s quarterly newsletter.

Individual Vanderbilt scores at all measurement times. (XLSX)

This dataset contains deidentified subject level data from the study titled: Responses to Exposure to Low Levels of Concentrated Ambient Particles in Healthy Young Adults (RECAP). Subject, exposure, and health endpoint data are included in the dataset. Health endpoint data includes inflammatory, heart rate variability and cardiac repolarization, lung function, blood chemistry, and lipids measures. This dataset is associated with the following publication: Wyatt, L., R. Devlin, A. Rappold, and M. Case. Low levels of fine particulate matter increase vascular damage and reduce pulmonary function in young healthy adults. Particle and Fibre Toxicology. BioMed Central Ltd, London, UK, 17(1): 58, (2020).

IntroductionObjective data on the association of maximal aerobic capacity (VO2max) with work related physical activity are sparse. Thus, it is not clear whether occupational physical activity (OPA) contributes to an increase of VO2max. This study examined the association of VO2max with work and non-work related physical activity in a Swiss working population.MethodsIn this cross-sectional study, a total of 337 healthy and full-time employed adults were recruited. Demographic data, height, weight and BMI were recorded in all subjects. Participants were classified into nine occupational categories (ISCO-88) and merged into three groups with low, moderate, and high OPA. Physical activity was objectively measured by the SenseWear Mini Armband on seven consecutive days (23 hours per day). Participants were regarded as sufficiently active when accumulating ≥30 min of moderate-to-vigorous physical activity per day. VO2max was evaluated using the multistage 20-meter shuttle run test.ResultsData of 303 participants were considered for analysis (63% male, age 33 yrs, SD 12). Multiple linear regression analysis (adjusted R2 = 0.69) revealed significant positive associations of VO2max with leisure-time physical activity (LTPA) at vigorous intensity (β = 0.212) and sufficient moderate-to-vigorous physical activity (β = 0.100) on workdays. Female gender (β = -0.622), age (β = -0.264), BMI (β = -0.220), the ratio of maximum to resting heart rate (β = 0.192), occupational group (low vs. high OPA, β = -0.141), and smoking (β = -0.133) were also identified as independent predictors of VO2max.ConclusionsThe present results suggest that VO2max is positively associated with LTPA, but not with OPA on workdays. This finding emphasizes the need for employees to engage in sufficient high-intensity physical activity in recreation for maintaining or improving VO2max with regard to health benefits.

Patients often provide untruthful information about their health to avoid embarrassment, evade treatment, or prevent financial loss. Privacy disclosures (e.g. HIPAA) intended to dissuade privacy concerns may actually increase patient lying. We used new mouse tracking-based technology to detect lies through mouse movement (distance and time to response) and patient answer adjustment in an online controlled study of 611 potential patients, randomly assigned to one of six treatments. Treatments differed in the notices patients received before health information was requested, including notices about privacy, benefits of truthful disclosure, and risks of inaccurate disclosure. Increased time or distance of device mouse movement and greater adjustment of answers indicate less truthfulness. Mouse tracking revealed a significant overall effect (p < 0.001) by treatment on the time to reach their final choice. The control took the least time indicating greater truthfulness and the privacy + r..., The data were collected as part of a controlled experiment using Amazon Mechanical Turk, Qualtrics, and JavaScript-based mouse tracking technology.,

The English Longitudinal Study of Ageing (ELSA) Covid-19 study can be seen as a follow-up study based on the sample of the regular ELSA study (held under SN 5050). ELSA was launched in 2002 with the primary objective of exploring ageing in England through the operationalisation of a longitudinal design, where repeated measures are taken over time from the same sample of study participants, composed of people aged 50 or above.

After the beginning of the Coronavirus Disease 2019 (COVID-19) outbreak at the end of 2019, its classification as global pandemic by the World Health Organisation in March 2020 and the gradual escalation of protective measures in the UK, culminating with the enforcement of a nation-wide lockdown in late March, the ELSA research team identified the need to carry out a new ad-hoc study that measures the socio-economic effects/psychological impact of the lockdown on the aged 50+ population of England.

Acknowledgment statement:

The ELSA COVID-19 Substudy was funded through the Economic and Social Research Council via the UK Research and Innovation Covid-19 Rapid Response call. Funding has also been received from the National Institute of Aging in the US, and a consortium of UK government departments coordinated by the National Institute for Health Research.

Further information can be found on the http://www.elsa-project.ac.uk/covid-19" style="background-color: rgb(255, 255, 255);">ELSA COVID-19 Study webpage.

ELSA COVID-19 study: End User Licence and Special Licence data

The main data and documentation for the ELSA COVID-19 study are available under SN 8688, subject to standard End User Licence conditions. This study (SN 8918) contains only interview week variables, which are subject to stringent Special Licence conditions. Users should obtain SN 8688 and check whether it is suitable for their needs before considering an application for this study (SN 8918).

Comparison of identified cases by conventional and fusion models.

This dataset contains all data and code required to clean the data, fit the models, and create the figures and tables for the laboratory experiment portion of the manuscript:Kannan, N., Q. D. Read, and W. Zhang. 2024. A natural polymer material as a pesticide adjuvant for mitigating off-target drift and protecting pollinator health. Heliyon, in press. https://doi.org/10.1016/j.heliyon.2024.e35510.In this dataset, we archive results from several laboratory and field trials testing different adjuvants (spray additives) that are intended to reduce particle drift, increase particle size, and slow down the particles from pesticide spray nozzles. We fit statistical models to the droplet size and speed distribution data and statistically compare different metrics between the adjuvants (sodium alginate, polyacrylamide [PAM], and control without any adjuvants). The following files are included:RawDataPAMsodAlgOxfLsr.xlsx: Raw data for primary analysesOrganizedDataPaperRevision20240614.xlsx: Raw data to produce density plots presented in Figs. 8 and 9raw_data_readme.md: Markdown file with description of the raw data filesR_code_supplement.R: All R code required to reproduce primary analysesR_code_supplement2.R: R code required to produce density plots presented in Figs. 8 and 9Intermediate R output files are also included so that tables and figures can be recreated without having to rerun the data preprocessing, model fitting, and posterior estimation steps:pam_cleaned.RData: Data combined into clean R data frames for analysisvelocityscaledlogdiamfit.rds: Fitted brms model object for velocitylnormfitreduced.rds: Fitted brms model object for diameter distributionemm_con_velo_diam_draws.RData: Posterior distributions of estimated marginal means for velocityemm_con_draws.RData: Posterior distributions of estimated marginal means for diameter distributionThe following software and package versions were used:R version 4.3.1CmdStan version 2.33.1R packages:brms version 2.20.5cmdstanr version 0.5.3fitdistrplus version 1.1-11tidybayes version 3.0.4emmeans version 1.8.9

This survey screened for a range of mental health conditions, including common mental health conditions (using the CIS-R), attention deficit hyperactivity disorder (ADHD, ASRS), posttraumatic stress disorder (PTSD, PCL-C), signs of dependence on drugs and alcohol (AUDIT), gambling harms (PGSI), personality disorder (SAPAS, SCID-II Q) and bipolar disorder (MDQ). Clinical examinations assessed autism (ADOS), psychotic disorders (SCAN) and eating disorders (SCAN ED). See the relevant chapters for further details on each condition or health behaviour and how it was examined.

China R & D: Expenditure: HT: Medical Equipment & Meter data was reported at 58,519.190 RMB mn in 2023. This records an increase from the previous number of 56,590.220 RMB mn for 2022. China R & D: Expenditure: HT: Medical Equipment & Meter data is updated yearly, averaging 15,687.380 RMB mn from Dec 2001 (Median) to 2023, with 23 observations. The data reached an all-time high of 58,519.190 RMB mn in 2023 and a record low of 510.000 RMB mn in 2001. China R & D: Expenditure: HT: Medical Equipment & Meter data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Business and Economic Survey – Table CN.OS: Research and Development: Expenditure.

Dataset from Méndez-López F, Oliván-Blázquez B, Domínguez García M, López-Del-Hoyo Y, Tamayo-Morales O, Magallón-Botaya R. Depressive and Anxious Symptoms Increase with Problematic Technologies Use Among Adults: The Effects of Personal Factors Related to Health Behavior. Psychol Res Behav Manag. 2023;16:2499-2515

Background: Depression and anxiety disorders are a significant and growing health problem that has a significant impact on psychosocial functioning and quality of life. The onset and severity of mental health problems have been related to various biological, psychosocial, and behavioral variables.Purpose: The purpose of this study was to explore the association among the severity of depression and anxiety, problematic information and communications technology (ICT) use, and some related personal factors with health behavior among adults. It also analyzes the moderating role of personal factors in the relationship between the problematic use of ICT and anxiety and depression.Patients and Methods: Descriptive, bivariate, multivariate and moderation analyzes of data from 391 participants of 35– 74 years old in primary health care centers located in Aragón (Spain) were performed between July 2021 and July 2022. The primary outcome was the severity of depressive and anxious symptoms as continuous variable.Results: Low sense of coherence (β = − 0.058; p =  0.043), low self-esteem (β = − 0.171; p=0.002), and low self-efficacy (β = − 0.122; p=  0.001), are predictors of having more severe depressive symptoms. Furthermore, low self-esteem (β = − 0.120; p=  0.012), low self-efficacy (β = − 0.092; p=0.004), and high problematic use of ICT (β =  0.169; p =  0.001), are predictors of having more severe anxiety symptoms. Moderation analyzes were significant in the effect of self-efficacy (b = − 0.040, p=0.001) and resilience (b = − 0.024, p=0.033) on the relationship between problematic ICT use and anxiety.Conclusion: The problematic use of ICT and personal factors are related to depressive and anxiety symptoms. The interrelationship between problematic ICT use, personal factors, and depression needs to be further explored.Keywords: depression, anxiety, problematic information and communication technologies use, technology addiction, personal health factors

Abstract Background Studies on “Spirituality, religion and health” (R/S) have been increasing worldwide, including in Brazil. Mapping this production can help researchers to understand this field and also to identify gaps in the Brazilian R/S studies. Objective To analyze the Brazilian scientific articles on “Religion, Spirituality and Health” available on the main electronic databases using a bibliometric approach. Methods A comprehensive review of four major databases (PubMed, Scopus, BVS and Web of Science) was conducted. Three reviewers performed the data analysis. Off-topic articles, articles from Portugal, books and thesis were excluded. Articles were then classified by: Publication year, journal, Central focus in R/S, Academic Area, Main topic and Study Type. Results From 3,963 articles found, 686 studies were included in the final analysis (320 had central focus on R/S). There was an increase of articles in the last decade (most observational), with predominance of mental health issues, and from journals in the field of psychiatry, public health and nursing. Discussion This study enabled us to widen our understanding about how the field of “spirituality, religion and health” has been established and how this field is increasing in Brazil. These findings can help in the development of future Brazilian studies.

In 2023, single coverage health insurance for employees cost more than ***** U.S. dollars for the year. this figure has increase every year since 2000, with the average annual cost of health insurance for singles being ***** in 2000.

MIMIC (Multiple Indicator and MultIple Causes) models afford powerful claims about measurement – particularly in identifying biased items – and are relatively simple to specify and test. Therefore, we argue that MIMIC models are sorely underutilized and serve important roles in ensuring sound and fair measurement in educational scholarship. When viewed from the perspective of Critical Quantitative (CritQuant) methodology, MIMICs hold promise in fostering equity-oriented and anti-racist measurement. The MIMIC strategy, employed from a CritQuant perspective, may also reveal how bias in measurement may lead to underestimating the impacts of racism on educational and related outcomes. To increase their use, this primer aims to explain how to specify and evaluate MIMIC models and provides sample code in R (lavaan) and MPlus. Public use data from the AddHealth study were used for these analyses, and are made available in this repository. The paper concludes with articulating the advantages and disadvantages of MIMICs, from both CritQuant and technical perspectives, to inform educational research.