In the United States, the prevalence of mental illness in the past year is more common among females than males and more common among the young than the old. As of 2024, some 26.7 percent of females reported some type of mental illness in the past year, compared to 20 percent of males. Common forms of mental illness include depression, anxiety disorders, and mood disorders. Depression Depression is one of the most common mental illnesses in the United States. Depression is defined by prolonged feelings of sadness, hopelessness, and despair leading to a loss of interest in activities once enjoyed, a loss of energy, trouble sleeping, and thoughts of death or suicide. It is estimated that around five percent of the U.S. population suffers from depression. Depression is more common among women with around six percent of women suffering from depression compared to four percent of men. Mental illness and substance abuse Data has shown that those who suffer from mental illness are more likely to suffer from substance abuse than those without mental illness. Those with mental illness are more likely to use illicit drugs such as heroin and cocaine, and to abuse prescription drugs than those without mental illness. As of 2023, around 7.9 percent of adults in the United States suffered from co-occuring mental illness and substance use disorder.

About Dataset

Overview

This dataset contains mental health survey responses from 600 students across 20 Bangladeshi universities (2022-2025), analyzing depression, anxiety, academic stress, and lifestyle factors affecting student well-being.

Data Description

The dataset contains 600 records with 16 features covering demographic, academic, and mental health dimensions:

• Timestamp: Date and time of survey response (2022-2025)
• Gender: Respondent's gender (Male/Female)
• Age: Student's age (18-26 years)
• Division: Geographic division in Bangladesh (8 divisions)
• University: One of 20 premier Bangladeshi universities
• Living Situation: Accommodation type (Dormitory/With Family/Alone/With Roommates)
• Course: Academic discipline (40+ fields including Engineering, Medicine, Arts, Sciences)
• Year of Study: Academic level (Year 1-4)
• CGPA: Academic performance ranges (0-4.00 scale)
• Financial Stress: Economic pressure level (Low/Medium/High)
• Marital Status: Relationship status (Yes/No)
• Depression: Self-reported depression status (Yes/No)
• Anxiety: Self-reported anxiety status (Yes/No)
• Panic Attack: Self-reported panic attack experiences (Yes/No)
• Family History Mental Illness: Genetic predisposition (Yes/No)
• Seek Treatment: Professional help-seeking behavior (Yes/No)

Key Highlights

• Comprehensive Coverage: Data from 20 top universities across all 8 divisions of Bangladesh
• Multi-dimensional Analysis: Combines academic, demographic, and mental health variables
• Real-world Relevance: Captures authentic student experiences with realistic missing data
• Longitudinal Scope: Survey responses collected over 4 years (2022-2025)
• Actionable Insights: Identifies risk factors and help-seeking patterns

Acknowledgements

Special thanks to the participating students from different universities who voluntarily shared their responses. Their cooperation made this dataset possible.

Dataset curated and organized by Monowar Islam for research and educational purposes.

Inspiration

To create awareness about student mental health and encourage open research into predictive mental health analytics, especially in developing nations like Bangladesh where such data is scarce yet vital for shaping future educational and healthcare policies.

The data are organized by OMH Region‐specific (Region of Provider), program type, and by the following demographic characteristics of the clients served during the week of the survey: sex (Male, Female, X (Non-binary), and Unknown), Transgender (No, Not Transgender; Yes, Transgender and Unknown), age (below 17 (Child), 18 and above(Adult) and unknown age) and race (White only, Black Only, Multi‐racial, Other and Unknown race) and ethnicity (Non‐Hispanic, Hispanic, Client Did Not Answer and Unknown). Persons with Hispanic ethnicity are grouped as “Hispanic,” regardless of race or races reported.

Ten to Men: The Australian Longitudinal Study on Male Health was commissioned by the Department of Health and Aged Care following the 2010 National Male Health Policy, and currently serves the National Men’s Health Strategy 2020-2030. This is Australia’s first national longitudinal study that focuses exclusively on male health and wellbeing. The cohort was recruited using a stratified, multi-stage & cluster sampling design to select males aged 10–55 years. Recruitment of eligible participants and Wave 1 of the data collection occurred between October 2013 and July 2014, resulting in a reconciled sample size of 16,021. The survey content was structured around six key research domains relevant to male health: wellbeing and mental health, use of health services, health-related behaviours, health status, health knowledge and social determinants. Wave 2 of the data collection occurred between November 2015 and May 2016. The sample size for Wave 2 was 11,936. The Wave 2 questionnaires largely retained Wave 1 items to obtain repeat longitudinal measures. New items added included additional questions on relationships, mental health, health literacy, help-seeking and resilience. Release 2.1 comprised of updated Wave 1 and Wave 2 datasets. These datasets have undergone changes to previous releases, including the renaming of variables, confidentialisation and other modifications. Release 2.1 offers General Release and Restricted Release. Wave 3 of the data collection occurred between July 2020 and February 2021. The sample size for Wave 3 was 7,919. The Wave 3 questionnaires largely retained items from previous waves to obtain repeat longitudinal measures. New items added included new questions on gambling, use of e-cigarettes, illicit drug use, gender identity, generalised anxiety, relationship quality, individual income, COVID-19 impact and natural disaster impact. Release 3.0 offers General Release and Restricted Release and linked MBS and PBS datasets. Wave 4 of the data collection occurred between August 2022 and December 2022. The sample size for Wave 4 was 7,050. The Wave 4 questionnaires largely retained items from previous waves to obtain repeat longitudinal measures. New items added included new questions on health conditions, masculinity, fathering ethnicity, gender & sexuality, intimidate partner violence, and injuries. Release 4.0 offers General Release and Restricted Release and linked MBS and PBS datasets. Release 4.0.1 is the most recent data release and offers updates to all waves of the General Release and Restricted Release datasets as explained in Change Log Registry.

Originally, the dataset come from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to gather data on the health status of U.S. residents. As the CDC describes: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.". The most recent dataset (as of February 15, 2022) includes data from 2020. It consists of 401,958 rows and 279 columns. The vast majority of columns are questions asked to respondents about their health status, such as "Do you have serious difficulty walking or climbing stairs?" or "Have you smoked at least 100 cigarettes in your entire life? [Note: 5 packs = 100 cigarettes]".

To improve the efficiency and relevance of our analysis, we removed certain attributes from the original BRFSS dataset. Many of the 279 original attributes included administrative codes, metadata, or survey-specific variables that do not contribute meaningfully to heart disease prediction—such as respondent IDs, timestamps, state-level identifiers, and detailed lifestyle questions unrelated to cardiovascular health. By focusing on a carefully selected subset of 18 attributes directly linked to medical, behavioral, and demographic factors known to influence heart health, we streamlined the dataset. This not only reduced computational complexity but also improved model interpretability and performance by eliminating noise and irrelevant information. All predicting variables could be divided into 4 broad categories:

1. Demographic factors: sex, age category (14 levels), race, BMI (Body Mass Index)

2. Diseases: weather respondent ever had such diseases as asthma, skin cancer, diabetes, stroke or kidney disease (not including kidney stones, bladder infection or incontinence)

3. Unhealthy habits:

   • Smoking - respondents that smoked at least 100 cigarettes in their entire life (5 packs = 100 cigarettes)
   • Alcohol Drinking - heavy drinkers (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week

4. General Health:

   • Difficulty Walking - weather respondent have serious difficulty walking or climbing stairs
   • Physical Activity - adults who reported doing physical activity or exercise during the past 30 days other than their regular job
   • Sleep Time - respondent’s reported average hours of sleep in a 24-hour period
   • Physical Health - number of days being physically ill or injured (0-30 days)
   • Mental Health - number of days having bad mental health (0-30 days)
   • General Health - respondents declared their health as ’Excellent’, ’Very good’, ’Good’ ,’Fair’ or ’Poor’

Below is a description of the features collected for each patient:

Background: A cancer patient's quality of life (QoL) is the perception of their physical, functional, psychological, and social well-being as well as their mental and emotional state. QoL is one of the most important factors to consider when a person is being treated for cancer and during follow-up. The present study aimed to understand the status of QoL of cancer patients and determine the factors affecting it. Methods: This cross-sectional study was conducted among 210 cancer patients attending the oncology unit of a medical college, within a 4-month consecutive time period in 2022. Data were collected by using the Bengali version of the European Organization for Research and Treatment of Cancer questionnaire. Results: The present study reported a high number of female cancer patients (67.6%). Breast cancer was more common among females (31.43%) while lung and upper respiratory tract cancer was among males (19.05). Most of the patients in the present study were diagnosed with cancer in the past year (86.19%). The functional scales' overall mean scores varied from 54.92 for physical functioning to 38.89 for social functioning. The highest symptom scale score was for financial issues (63.02), while the lowest was for diarrhea (33.01). The overall QoL of cancer patients in the present study was 47.98 which was 45.71 for males and 49.10 for females respectively.
Conclusion: The overall QoL was poor in cancer patients in the present study compared to the developed countries. There was a low score for QoL for social and emotional function. Financial difficulty was the primary reason behind low QoL in the symptom scale. If the government supports cancer patients by providing subsidies for treatment and health insurance policies, cancer patients will benefit and QoL will improve. Methods The study proposal and consent form were approved by the Ethics Committee. The present study was conducted in the Oncology Unit of a medical college within a 4-month consecutive time period in 2022. The expected number of new cancer patients visiting the department was 400 during the study period. We chose p = 0.50, q = 0.50, Z = 1.96, and E = 0.04 for N = 400, and the minimum sample size was calculated to be n = 196. For the purpose of this study, permission was sought from European Organization for Research and Treatment of Cancer (EORTC) to use the Bengali version of their EORTC QLQ C30 questionnaire. EORTC provided the research tool and scoring manuals for the study. The 30-item questionnaire covers 15 domains which consist of five functioning scales (physical functioning, social functioning, role functioning, emotional functioning, and cognitive functioning) and nine symptom scales (fatigue, pain, nausea/vomiting, dyspnea, sleep disturbances, appetite loss, diarrhea, constipation, and financial difficulties) and one global health status/ quality of life scale.(Aaronson et al., 1993) Strong scores on the functioning and global health status/QoL scales on the 100-point meter suggest high QoL, whereas high scores on the symptom scales indicate a high symptom burden.(Fayers PM et al., 2001) Data were collected 2 days each week. All adult patients who came to the outpatient clinic and all patients newly admitted to the inpatient clinic on those days were administered the questionnaire in person by the first author. The study objective was explained to the patients and verbal consent was obtained. Patients who were interviewed for this study previously, those who could not provide consent (unconscious), patients with suspected cancer but without a confirmed report, and patients less than 18 years of age were excluded. Socio-demographic characteristics such as age at treatment, gender, marital status, religion, economic status, and education were obtained from the patients. The information on clinical status such as the site of the primary tumor, stage of the tumor, and type of treatment was recorded from the clinical documentation.

Globally, gender differences are well-documented in alcohol use behaviors and MDD, yet these remain understudied in Moshi, Tanzania. Understanding gender-specific nuances of these conditions is crucial for developing effective and culturally appropriate mental health treatments. This study aims to investigate gender differences in MDD, alcohol use, and other aspects of mental well-being among patients at Kilimanjaro Christian Medical Centre (KCMC). Six hundred and seventy-six patients presenting for care at the KCMC Emergency Department (ED) and Reproductive Health Centre (RHC) were enrolled between October 2021 and May 2022. Patients were selected through systematic random sampling and completed quantitative surveys, including the Alcohol Use Disorder Identification Test (AUDIT) and the Patient Health Questionnaire 9 (PHQ-9). Nineteen patients were purposively chosen from the study population for in-depth interviews (IDIs) exploring alcohol use, gender, and depression. ANOVA, chi-squared tests, adjusted log-binomial regressions, and a linear regression model were used to analyze quantitative data in RStudio. A grounded theory approach was used to analyze all IDIs in NVivo. Average [SD] PHQ-9 scores were 7.22 [5.07] for ED women, 4.91 [4.11] for RHC women, and 3.75 [4.38] among ED men. ED women held the highest prevalence of MDD (25%) compared to RHC women (11%) and ED men (7.9%) (p < 0.001). Depressive symptoms were associated with higher AUDIT scores for ED men (R2 = 0.11, p < 0.001). Qualitative analysis showed that while present for women, social support networks were notably absent for men, playing a role in alcohol use. For men, alcohol was described as a coping mechanism for stress. Intersectionality of gender, alcohol use, and depression is influenced by sociocultural and behavioral norms in Moshi. As such, multi-layered, gender-differentiated programming should be considered for the treatment of substance use and mental health conditions in this region.

Ten to Men: The Australian Longitudinal Study on Male Health was commissioned by the Department of Health, Disability and Ageing following the 2010 National Male Health Policy and currently serves the National Men’s Health Strategy 2020-2030. This is Australia’s first national longitudinal study that focuses exclusively on male health and wellbeing. The cohort was recruited using a stratified, multi-stage & cluster sampling design to select males aged 10–55 years. Recruitment of eligible participants and Wave 1 of the data collection occurred between October 2013 and July 2014, resulting in a reconciled sample size of 16,021. The survey content was structured around six key research domains relevant to male health: wellbeing and mental health, use of health services, health-related behaviours, health status, health knowledge and social determinants. Wave 2 of the data collection occurred between November 2015 and May 2016. The sample size for Wave 2 was 11,936. The Wave 2 questionnaires largely retained Wave 1 items to obtain repeat longitudinal measures. New items included additional questions on relationships, mental health, health literacy, help-seeking and resilience. Release 2.1 comprised of updated Wave 1 and Wave 2 datasets. These datasets have undergone changes to previous releases, including the renaming of variables, confidentialisation and other modifications. Release 2.1 offers General Release and Restricted Release. Wave 3 of the data collection occurred between July 2020 and February 2021. The sample size for Wave 3 was 7,919. The Wave 3 questionnaires largely retained items from previous waves to obtain repeat longitudinal measures. New items included new questions on gambling, use of e-cigarettes, illicit drug use, gender identity, generalised anxiety, relationship quality, individual income, COVID-19 impact and natural disaster impact. Release 3.0 offers General Release, Restricted Release and linked Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) datasets. Wave 4 of the data collection occurred between August 2022 and December 2022. The sample size for Wave 4 was 7,050. The Wave 4 questionnaires largely retained items from previous waves to obtain repeat longitudinal measures. New items included new questions on health conditions, masculinity, fathering, ethnicity, gender & sexuality, intimate partner violence, and injuries. Release 4.0 offers General Release, Restricted Release and linked MBS and PBS datasets. Release 4.0.1 offers updates to all waves of the General Release and Restricted Release datasets as explained in the Change Log Registry. Wave 5 of the data collection occurred between August 2024 and March 2025. The sample size for Wave 5 was 13,182. The increased sample size for Wave 5 is due to the addition of participants from the sample top-up cohort, recruited between 2022 and 2024, who participated in a wave for the first time. Also, whilst the original cohort fieldwork period concluded in December 2024, an additional recruitment period was required and occurred from January 2025 to March 2025, with this cohort participating directly into Wave 5 data collection. The Wave 5 questionnaires largely retained items from previous waves to obtain longitudinal consistency in measures. New items included questions on reasons for vaping/e cigarette use, pornography use, alcohol use, an adapted depression measure specifically for Aboriginal and/or Torres Strait Islander men, economic abuse, Australian Defence Force status, country of birth, role models, adverse childhood events and health screening. Release 5.0 offers General Release, Restricted Release and linked datasets for MBS and PBS. New linked datasets for Wave 5 include Centrelink and National Death Index – Cause of Death. Release 5.0 is the most recent data release and offers updates to all waves of the General Release and Restricted Release datasets as explained in the Change Log Registry.

BackgroundDepression and anxiety (D&A) are currently recognized as complex and prevalent mental disorders that pose major threats to mental health. Women are more susceptible to D&A than men.MethodsWe collected data from female participants in Shanxi Province between November 2021 and March 2022 through on-site investigations and an online survey. The survey collected information on sociodemographic traits, lifestyle factors, and physical and mental health. The degree of D&A was evaluated using the Center for Epidemiological Studies Depression Scale (CESD-10) and the Generalized Anxiety Disorder Assessment Scale (GAD-7). We assessed the impact of these factors on D&A symptoms among women using regression and fuzzy-set qualitative comparative analysis (fsQCA).ResultsD&A symptoms had many common influencing factors. Regression analysis identified key protective factors against D&A, including better self-rated health (Depression: OR = 0.11, 95% CI = 0.03–0.47; Anxiety: OR = 0.11, 95% CI = 0.02–0.57) and the absence of recent illness (Depression: OR = 0.56, 95% CI = 0.38–0.83; Anxiety: OR = 0.49, 95% CI = 0.35–0.70). Age exhibited marginal protective effects for both conditions (OR = 0.99, 95% CI = 0.98–1.00). In contrast, occupational stress constituted a significant risk factor, substantially increasing the likelihood of depression (OR = 2.66, 95% CI = 1.43–4.96) and anxiety (OR = 2.99, 95% CI = 1.43–4.96). FsQCA analysis did not identify the conditions for ideal mental health (all consistency < 0.9). However, it did identify eight condition configurations predicting mental health (absence of depression symptoms), each achieving consistency ≥0.87. Additionally, two distinct configurations explained resilience to anxiety (consistency ≥0.80). All configurations met fsQCA’s consistency requirements, with self-rated health (present in 10/10 pathways), social support (9/10), and marital status (9/10) playing important roles in most configurations.ConclusionWomen’s mental health faces significant challenges, with D&A being closely intertwined. FsQCA did not identify any specific condition for the absence of D&A symptoms. However, it revealed multiple pathways to mental well-being, highlighting the need for personalized, multifactorial interventions rather than a one-size-fits-all approach. Regression and fsQCA complement each other, offering unique strengths, and their combined insights should be widely applied to broader research and practice.

This is the underlying data from a 2022 evaluation of a group-based intervention that draws on affirmative cognitive behavioral therapy (CBT) strategies for men who have sex with men (MSM) and transgender women (TGW) at risk for or living with HIV in Lagos, Nigeria. The data set comprises four (4) rounds of survey data in this delayed intervention group randomized controlled trial: (1) baseline (immediate and delayed group), (2) post (immediate group), (3) post (delayed group), and (4) three-month follow-up (immediate group only). The intervention consisted of four weekly in-person group sessions each 2.5-3 hours in length, facilitated by community health workers. There were 240 participants in trial, which was supported by the Elton John AIDS Foundation.

Abstract

The 2022 Nepal Demographic and Health Survey (NDHS) is the sixth survey of its kind implemented in the country as part of the worldwide Demographic and Health Surveys (DHS) Program. It was implemented by New ERA under the aegis of the Ministry of Health and Population (MoHP) of the Government of Nepal with the objective of providing reliable, accurate, and up-to-date data for the country.

The primary objective of the 2022 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2022 NDHS collected information on fertility, marriage, family planning, breastfeeding practices, nutrition, food insecurity, maternal and child health, childhood mortality, awareness and behavior regarding HIV/AIDS and other sexually transmitted infections (STIs), women’s empowerment, domestic violence, fistula, mental health, accident and injury, disability, and other healthrelated issues such as smoking, knowledge of tuberculosis, and prevalence of hypertension.

The information collected through the 2022 NDHS is intended to assist policymakers and program managers in evaluating and designing programs and strategies for improving the health of Nepal’s population. The survey also provides indicators relevant to the Sustainable Development Goals (SDGs) for Nepal.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men ageed 15-49, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2022 NDHS is an updated version of the frame from the 2011 Nepal Population and Housing Census (NPHC) provided by the National Statistical Office. The 2022 NDHS considered wards from the 2011 census as sub-wards, the smallest administrative unit for the survey. The census frame includes a complete list of Nepal’s 36,020 sub-wards. Each sub-ward has a residence type (urban or rural), and the measure of size is the number of households.

In September 2015, Nepal’s Constituent Assembly declared changes in the administrative units and reclassified urban and rural areas in the country. Nepal is divided into seven provinces: Koshi Province, Madhesh Province, Bagmati Province, Gandaki Province, Lumbini Province, Karnali Province, and Sudurpashchim Province. Provinces are divided into districts, districts into municipalities, and municipalities into wards. Nepal has 77 districts comprising a total of 753 (local-level) municipalities. Of the municipalities, 293 are urban and 460 are rural.

Originally, the 2011 NPHC included 58 urban municipalities. This number increased to 217 as of 2015. On March 10, 2017, structural changes were made in the classification system for urban (Nagarpalika) and rural (Gaonpalika) locations. Nepal currently has 293 Nagarpalika, with 65% of the population living in these urban areas. The 2022 NDHS used this updated urban-rural classification system. The survey sample is a stratified sample selected in two stages. Stratification was achieved by dividing each of the seven provinces into urban and rural areas that together formed the sampling stratum for that province. A total of 14 sampling strata were created in this way. Implicit stratification with proportional allocation was achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units at the different levels, and by using a probability-proportional-to-size selection at the first stage of sampling. In the first stage of sampling, 476 primary sampling units (PSUs) were selected with probability proportional to PSU size and with independent selection in each sampling stratum within the sample allocation. Among the 476 PSUs, 248 were from urban areas and 228 from rural areas. A household listing operation was carried out in all of the selected PSUs before the main survey. The resulting list of households served as the sampling frame for the selection of sample households in the second stage. Thirty households were selected from each cluster, for a total sample size of 14,280 households. Of these households, 7,440 were in urban areas and 6,840 were in rural areas. Some of the selected sub-wards were found to be overly large during the household listing operation. Selected sub-wards with an estimated number of households greater than 300 were segmented. Only one segment was selected for the survey with probability proportional to segment size.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used in the 2022 NDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Nepal. In addition, a self-administered Fieldworker Questionnaire collected information about the survey’s fieldworkers.

Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organizations, and international donors. After all questionnaires were finalized in English, they were translated into Nepali, Maithili, and Bhojpuri. The Household, Woman’s, and Man’s Questionnaires were programmed into tablet computers to facilitate computer-assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose any of the three languages for each questionnaire. The Biomarker Questionnaire was completed on paper during data collection and then entered in the CAPI system.

Cleaning operations

Data capture for the 2022 NDHS was carried out with Microsoft Surface Go 2 tablets running Windows 10.1. Software was prepared for the survey using CSPro. The processing of the 2022 NDHS data began shortly after the fieldwork started. When data collection was completed in each cluster, the electronic data files were transferred via the Internet File Streaming System (IFSS) to the New ERA central office in Kathmandu. The data files were registered and checked for inconsistencies, incompleteness, and outliers. Errors and inconsistencies were immediately communicated to the field teams for review so that problems would be mitigated going forward. Secondary editing, carried out in the central office at New ERA, involved resolving inconsistencies and coding the open-ended questions. The New ERA senior data processor coordinated the exercise at the central office. The NDHS core team members assisted with the secondary editing. The paper Biomarker Questionnaires were compared with the electronic data file to check for any inconsistencies in data entry. The pictures of vaccination cards that were captured during data collection were verified with the data entered. Data processing and editing were carried out using the CSPro software package. The concurrent data collection and processing offered a distinct advantage because it maximized the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for effective monitoring. The secondary editing of the data was completed by July 2022, and the final cleaning of the data set was completed by the end of August.

Response rate

A total of 14,243 households were selected for the sample, of which 13,833 were found to be occupied. Of the occupied households, 13,786 were successfully interviewed, yielding a response rate of more than 99%. In the interviewed households, 15,238 women age 15-49 were identified as eligible for individual interviews. Interviews were completed with 14,845 women, yielding a response rate of 97%. In the subsample of households selected for the men’s survey, 5,185 men age 15-49 were identified as eligible for individual interviews and 4,913 were successfully interviewed, yielding a response rate of 95%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors result from mistakes made in implementing data collection and in data processing, such as failing to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and entering the data incorrectly. Although numerous efforts were made during the implementation of the 2022 Nepal Demographic and Health Survey (2022 NDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 NDHS is only one of many samples that could have been selected from the same population, using the same design and expected sample size. Each of these samples would yield results that differ somewhat from the results of the selected sample. Sampling errors are a measure of the variability among all possible samples. Although the exact degree of variability is unknown, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, and so on), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the

Univariate Relative Risks for Predictors of MDD Status.

IntroductionIn recent years, the concept of epistemic trust has emerged as a critical factor in understanding psychopathology, particularly within the context of personality disorders. A self-report instrument, the Epistemic Trust, Mistrust, and Credulity Questionnaire (ETMCQ), has demonstrated its validity among English and Italian adult populations. However, extending its applicability to adolescents is essential for comprehending the role of epistemic trust in the development of mental disorders. The aim of this study was to validate the ETMCQ within the Italian adolescent demographic.MethodsData were gathered from a wide selection of middle and high schools across Italy. The data collection started on 01/03/2022 and ended on 30/06/2022. Besides the ETMCQ (Study 1 = 662 participants, 12–18 years old, M = 15.56, SD = 2.20; 324 females, 338 males), we also administered other self-report instruments measuring mentalization, emotional dysregulation, general levels of psychopathology, and interpersonal trust in a smaller groups (Study 2 = 417 participants, aged from 12–19 years old, M = 15.64; SD = 2.08; 249 females, 168 males).ResultsOur findings provide empirical validation for the theoretical framework concerning the role of epistemic trust in psychological functioning and substantiate the validity of ETMCQ as a measure to assess it among teenagers.ConclusionsThe ETMCQ is a valid and promising instrument for adolescent populations; its ease and brevity of administration could make it a valuable tool both in clinical and research contexts, shedding light on the role of epistemic trust in mental health.