White ethnicity included White (British), White (Irish) and White (other) and was predominantly White (British). Asian ethnicity included Asian (Indian), Asian (Pakistani), Asian (Bangladeshi) and Asian (other). Black ethnicity included Black (African), Black (Caribbean) and Black (other). SD, standard deviation.

1Referent: Secondary;2Referent: Not employed;3Referent: Taiwanese sample.*p = 0.03–0.02,**p = 0.01–0.001,†p

The incidence of HIV infection in rural African youth remains high despite widespread knowledge of the disease within the region and increasing funds allocated to programs aimed at its prevention and treatment. This suggests that program efficacy requires a more nuanced understanding of the profiles of the most at-risk individuals. To evaluate the explanatory power of novel psychographic variables in relation to high-risk sexual behaviors, we conducted a survey to assess the effects of psychographic factors, both behavioral and attitudinal, controlling for standard predictors in 546 youth (12–26 years of age) across 8 villages in northern Tanzania. Indicators of high-risk sexual behavior included HIV testing, sexual history (i.e., virgin/non-virgin), age of first sexual activity, condom use, and number of lifetime sexual partners. Predictors in the statistical models included standard demographic variables, patterns of media consumption, HIV awareness, and six new psychographic features identified via factor analyses: personal vanity, family-building values, ambition for higher education, town recreation, perceived parental strictness, and spending preferences. In a series of hierarchical regression analyses, we find that models including psychographic factors contribute significant additional explanatory information when compared to models including only demographic and other conventional predictors. We propose that the psychographic approach used here, in so far as it identifies individual characteristics, aspirations, aspects of personal life style and spending preferences, can be used to target appropriate communities of youth within villages for leading and receiving outreach, and to build communities of like-minded youth who support new patterns of sexual behavior.

The data was collected during the first and second waves of the COVID-19 pandemic among students of Opole University of Technology in Poland. The data includes some demographic characteristics of the study sample, as well as standardized psychological questionnaires to assess perceived stress (PSS-10), anxiety (GAD-7) and coping styles (CISS-48). The aim of the study is to examine the Multidimensional Interaction Model of Stress, Anxiety and Coping during the COVID-19 pandemic.

This data collection was designed to examine the effectiveness of a New York City agency's attempt to decrease the negative emotions that result from victimization. The data address the following questions: (1) To what extent do specific treatments mitigate the negative psychological impact of victimization? (2) Are individuals from a particular demographic group more prone to suffer from psychological adjustment problems following victimization? (3) When victimized, do individuals blame themselves or the situation? (4) Are some crimes more difficult to cope with than others? (5) Does previous victimization affect the likelihood that an individual will have difficulty coping with current as well as future victimization? Data were collected in two waves, with Wave 1 interviews completed within one month of the victimization incident and Wave 2 interviews completed three months after treatment. The effects of three treatments were measured. They included: traditional crisis counseling (which incorporates psychological aid and material assistance such as food, shelter, cash, etc.), cognitive restructuring (challenges to "irrational" beliefs about the world and one's self used in conjunction with crisis counseling), and material assistance only (no psychological aid provided). A fourth group of victims received no treatment or services. Three standardized psychometric scales were used in the study. In addition to these standardized scales, the initial assessment battery included an index of fear of crime as well as an index that measured behavior adjustment. Another set of measures assessed how victims perceived their experience of victimization and included items on self-blame, selective evaluation, and control. Also included were questions about the crime and precautions taken to guard against future victimization. The follow-up assessment battery was virtually identical to the initial battery, except that questions about services and social support received by the victim were added. The following demographic variables are included in the data: sex, age, marital status, education, income, and race. The unit of analysis was the individual.

The Common Cold Project began in 2011 with the aim of creating, documenting, and archiving a database that combines final research data from 5 prospective viral-challenge studies that were conducted over the preceding 25 years. The data collection includes the British Cold Study (BCS), which focused on psychological stress; the Pittsburgh Cold Study 1 (PCS1), which built on the BCS; the Pittsburgh Cold Study 2 (PCS2), which examined childhood socioeconomic status and personality; the Pittsburgh Mind-Body Center Cold Study (PMBC), which recorded detailed mood and behavior data over 14 days; the Pittsburgh Cold Study 3, which focused on childhood environment; the Pittsburg Cold Study 3 Social Rhythm Data (PCS3-SRM), which recorded daily interview data of mood, health behavior, and social interaction; and finally the 5 Study Aggregate, which was designed to facilitate analysis across studies. These studies assessed predictor (and hypothesized mediating) variables in healthy adults aged 18 to 55 years, experimentally exposed them to a virus that causes the common cold, and then monitored them for development of infection and signs and symptoms of illness. Standard control variables (covariates) included age, sex, socioeconomic status (SES), race/ethnicity, body mass index (BMI), season of the year, and specific antibody (Ab) titer to the challenge virus (specific immunity). Three of the studies also include daily evening interviews (conducted for 6 or 14 days before exposure to a virus and assessing daily social interactions, mood, health behaviors, and physical symptoms; and daily diaries collected during the quarantine period (1 day before and 5-6 days after viral exposure), including cold-specific and nonspecific symptoms, mood, and health behaviors. These data accompany datasets four, five, and seven. Many common variables were collected across 2 or more studies, and all 5 studies include measures of upper respiratory infectious illness (URI) (e.g., infection, signs and symptoms of a cold, local [nasal mucosa] release of pro- and anti-inflammatory cytokines). Data were also collected on a broad assortment of health-related outcomes not specific to URI including anthropomorphic measures (such as body mass index and waist circumference), complete blood cell counts and differentials, measures of functional immunity, self-reported and objectively assessed health behaviors (smoking, alcohol consumption, physical activity, diet, and sleep), measures of functional physiology across several biological systems (such as pulmonary function, resting cardiovascular function, endocrine, and metabolic activity), and self-reported assessments of physical and psychological health and well-being. In addition, the 5 studies collected data on an extensive range of demographic, health behavior, psychological and social variables including adult SES and subjective social standing, childhood SES, major stressful life events and perceived stress, personality, psychological expectations and beliefs, social relationships, and state and trait affect.

The PHQ-9 Enhanced Student Depression Dataset contains comprehensive responses from 682 students to the PHQ-9 questionnaire, a well-established clinical tool for diagnosing depression. This enhanced 5th edition represents a significant advancement from previous versions, incorporating additional psychosocial factors that influence mental health outcomes among young adults aged 17-26 years.

Important Note: This survey was conducted from the start under the supervision of qualified mental health professionals and clinical researchers, ensuring ethical data collection practices and participant welfare throughout the study.

PHQ-9 Assessment Framework The PHQ-9 questionnaire includes 9 standardized questions assessing depression symptoms over the past two weeks, covering mood, energy levels, sleep, appetite, concentration, and suicidal ideation. Responses are scored on a 4-point scale from 0 (Not at all) to 3 (Nearly every day), with total scores ranging from 0 to 27.

Depression severity is classified into five categories:

Minimal (0-4): 206 participants (30.2%) Mild (5-9): 155 participants (22.7%) Moderate (10-14): 128 participants (18.8%) Moderately Severe (15-19): 125 participants (18.3%) Severe (20-27): 68 participants (10.0%)

New in 5th Edition Key Improvements from Previous Editions Increased sample size from 400 to 682 participants (70% increase) Zero missing values across all 16 variables Professional supervision throughout data collection Enhanced ethical framework with IRB approval

New Psychosocial Variables Three critical stress factors were added based on validated correlations with depression severity: Sleep Quality: Good (34.9%), Average (31.5%), Bad (21.0%), Worst (12.6%) Study Pressure: Good (26.7%), Average (31.1%), Bad (26.5%), Worst (15.7%) Financial Pressure: Good (26.7%), Average (32.6%), Bad (25.5%), Worst (15.2%)

Demographics Age Range: 17-26 years (mean: 21.4) Gender: 418 males (61.3%), 264 females (38.7%)

Applications Clinical Research: Depression prediction models, multi-factor analysis, risk stratification Machine Learning: Multi-class classification, feature engineering, predictive analytics Education: Clinical training, research methodology, statistical analysis

Ethical Considerations All data collected under professional mental health supervision IRB approval obtained with informed consent protocols Crisis intervention procedures established All PII removed, maintaining strict anonymity

Participant support resources provided throughout study

This enhanced dataset provides a robust foundation for automated depression detection research while maintaining the highest standards of ethical data collection and clinical relevance for student populations.

Student Depression Dataset: Analyzing Mental Health Trends and Predictors Among Students

Overview
This dataset compiles a wide range of information aimed at understanding, analyzing, and predicting depression levels among students. It is designed for research in psychology, data science, and education, providing insights into factors that contribute to student mental health challenges and aiding in the design of early intervention strategies.

Data Description
- Format: CSV (each row represents an individual student)
- Features:
- ID: Unique identifier for each student
- Demographics: Age, Gender, City
- Academic Indicators: CGPA, Academic Pressure, Study Satisfaction
- Lifestyle & Wellbeing: Sleep Duration, Dietary Habits, Work Pressure, Job Satisfaction, Work/Study Hours
- Additional Factors: Profession, Degree, Financial Stress, Family History of Mental Illness, and whether the student has ever had suicidal thoughts
- Target Variable:
- Depression_Status: A binary indicator (0/1 or Yes/No) that denotes whether a student is experiencing depression

Key Highlights
- Multifaceted Data: Integrates demographic, academic, and lifestyle factors to offer a comprehensive view of student wellbeing.
- Ethical Considerations: Data collection adhered to strict ethical standards with an emphasis on privacy, informed consent, and anonymization.
- Research & Practical Applications: Ideal for developing predictive models, conducting statistical analyses, and informing mental health intervention strategies in educational environments.

Usage & Potential Applications
- Academic Research: Explore correlations between academic pressures and mental health trends.
- Data Science Projects: Build predictive models to identify at-risk students based on various indicators.
- Policy Making: Inform the development of targeted mental health support programs within academic institutions.

Ethical Note
Due to the sensitive nature of the data, please ensure that any analysis or published results respect privacy and ethical guidelines. Users of this dataset should be mindful of the ethical implications when interpreting and sharing insights.

Means and standard deviations (SD) of age-6 IQ for exposed persons classified by trauma type and presence or absence of PTSD.

The overall goal of this survey is to understand and grasp the dynamic situation of elderly people in urban and rural areas of China over time, as well as the new problems encountered by elderly people in the process of modernization. The main contents of the survey include: basic conditions and demographic and sociological characteristics of the elderly, economic support and security, medical and life care, community services, spiritual and cultural needs, mechanisms and forms of participation in social development, grassroots organizations and management. 2. Conduct in-depth analysis on the health status, living support, consumption needs, care services, community construction, home-based elderly care, and the demand for elderly work among the elderly population. 3. Strengthen scientific investigation and research on major issues such as social security for the elderly, legal rights and interests, spiritual and cultural life, participation in social development, and the elderly industry, in close conjunction with national aging work. To formulate aging policies and promote the development of the elderly industry, we will use the latest achievements in basic research fields such as social science, information science, and medical science to vigorously carry out basic theoretical research on aging applications, and provide necessary theoretical reserves for scientific research and innovation in the field of aging. 4. Write special research reports on topics such as elderly economy, elderly healthcare and care, elderly education, elderly spiritual and cultural life, elderly social participation, elderly law, elderly psychology, and the elderly industry. Key comparative studies will be conducted on the issues of elderly people and poverty, population aging and public policies, elderly people and grassroots organizations, social exclusion and social policies of the elderly, political tendencies of the elderly, population aging and social security costs, family elderly care in social wealth redistribution, China's aging policy and sustainable development, promoting intergenerational harmony, the impact of population aging on the aging industry, psychological research on the elderly, safeguarding the legitimate rights and interests of the elderly, elderly participation in social development and elderly social support networks, and community services for the elderly. This survey was conducted within the scope of 20 provinces, autonomous regions, and municipalities directly under the central government nationwide, and the standard time point for the survey was 00:00 on June 1, 2006. The survey subjects are divided into two types, with individual interviewees being Chinese citizens aged 60 and above residing within the territory of the People's Republic of China (excluding Taiwan Province, Hong Kong Special Administrative Region, and Macao Special Administrative Region); The community survey targets elderly related departments or elderly workers at all levels of the city (district, county), street (township, town), neighborhood committee (community, village committee).

The Coronavirus Disease 2019 (COVID-19) has had devastating effects globally. These effects are likely to result in mental health problems at different levels. Although studies have reported the mental health burden of the pandemic on the general population and frontline health workers, the impact of the disease on the mental health of patients in COVID-19 treatment and isolation centres have been understudied in Africa. We estimated the prevalence of depression and anxiety and associated risk factors in hospitalized persons with COVID-19. A cross-sectional survey was conducted among 489 patients with COVID-19 at the three government-designated treatment and isolation centres in Edo State, Nigeria. The 9-item Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder-7 (GAD-7) tool were used to assess depression and anxiety respectively. Binary logistic regression was applied to determine risk factors of depression and anxiety. Results Of the 489 participants, 49.1% and 38.0% had depressive and anxiety symptoms respectively. The prevalence of depression, anxiety, and combination of both were 16.2%, 12.9% and 9.0% respectively. Moderate-severe symptoms of COVID-19, ≥14 days in isolation, worrying about the outcome of infection and stigma increased the risk of having depression and anxiety. Additionally, being separated/divorced increased the risk of having depression and having comorbidity increased the risk of having anxiety. A substantial proportion of our participants experienced depression, anxiety and a combination of both especially in those who had the risk factors we identified. The findings underscore the need to address modifiable risk factors for psychiatric manifestations early in the course of the disease and integrate mental health interventions and psychosocial support into COVID-19 management guidelines. -- Methods Setting and study design A descriptive cross-sectional study was conducted from 15th April to 11th November 2020. The participants were COVID-19 Real Time-Reverse Transcriptase -Polymerase Chain Reaction (rRT-PCR) positive persons who were hospitalized at the three government- designated treatment and isolation centres in Edo State, Nigeria. Participants and data collection procedure All eligible and consenting persons who were COVID-19 rRT-PCR positive and hospitalized at any of the study institutions within the period of the survey were recruited. The inclusion criteria comprised of persons with confirmed COVID-19, hospitalized at any of the study institutions who consented to participate in the study and were eleven years and above. Exclusion criteria comprised of hospitalized persons who tested positive for COVID-19 but declined or were unable to give consent to participate in the study and persons below 11 years due to the inappropriateness of the assessment tools for anxiety and depression in this age group. Medical records/registers at the treatment and isolation centres were reviewed daily in order to identify new admissions and discharges in the centres and ineligible patients due to age (less than 11 years). A total of 796 persons with confirmed COVID-19 were hospitalized at the three government designated treatment and isolation centres in Edo State over the study period. All patients were informed and acknowledged a detailed description of the study, eligibility requirements and voluntariness to participate in the study. Nineteen of them were below 11 years and were excluded, and 265 patients either refused to give consent or were too ill (critically ill) to consent and participate in the study. A total of 512 were therefore recruited for the study. Semi-structured and structured questionnaires incorporating socio-demographics, basic clinical history/information and an assessment of anxiety and depression were administered to recruited participants on the fifth day of admission into the treatment and isolation centres. The questionnaires were self - administered except for those who opted for interviewer-administered questionnaires (mainly those with severe COVID-19 infection). Questionnaires were administered in the English language as all participants had some levels of formal education and were literate enough to understand the language. Those who were critically ill with COVID-19 infection were unable to consent and participate in the study. Online survey and hard copies of the questionnaires were made available for completion. All the participants preferred hard copies of the questionnaires and a copy of the signed consent form was retained by each participant and one by the researchers. Clinical information on severity of COVID-19 infection and presence and type of comorbidity were obtained from their medical records (case files). Length of stay in treatment and isolation centres was obtained from their case files after discharge from the centres as the questionnaires were coded for ease of identification. Measurements The socio-demographic/clinical history questionnaire This was designed to provide information about the participant’s age, gender, religion, marital status, employment status and the highest level of formal education. Clinical variables such as COVID-19 rRT-PCR status, previous/family history of mental illness, the severity of COVID-19 infection, the number of days in isolation, comorbidity were ascertained as well. To ascertain the worry factor, the question “what is your greatest worry about being COVID-19 positive” was asked. The 9-item Patient Health Questionnaire (PHQ‑9) This consists of nine items, each of which is scored 0 to 3, providing a 0 to 27 severity score.[15] PHQ‑9 severity is calculated by assigning scores of 0, 1, 2, and 3, to the response categories of: Not at all, several days, more than half the days, and nearly every day, respectively. It consists of the nine criteria for depression from the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM‑IV). The PHQ‑9 is comparable or superior in operating characteristics, and valid as both a diagnostic and severity measure.[16] Scores of 5, 10, 15, and 20 represent cut-off points for mild, moderate, moderately severe, and severe depression respectively. A PHQ-9 score of 10 or greater is recommended if a single screening cut-off is to be used, this cut-off point has a sensitivity for major depression of 88% and a specificity of 88%. The modified version for adolescents PHQ-A was used for participants within the ages of 11 and 17 years. A cut-off score of ≥ 10 was used to represent cases of depression. The PHQ-9 can be self-administered or clinician administered. The Generalized Anxiety Disorder-7 (GAD-7) This is a 7-item self-report questionnaire that allows for the rapid detection of GAD, the validity is not compromised if the clinician reads the questions to the client.[17] Participants are asked if they were bothered by anxiety-related problems over the past two weeks by answering seven items on a 4-point scale. The total scores range from 0 to 21. At a cut-off score of 10, the GAD-7 had a sensitivity of 89 % and a specificity of 82 % for detecting GAD compared with a structured psychiatric interview.[17] Notably, among clinical and general population samples, the GAD-7 has demonstrated good reliability and cross-cultural validity as a measure of GAD (16). Its use has been validated in adolescents.[18] A cut-off score of ≥ 10 was used to represent cases of anxiety. Ethics Ethical clearance was obtained from our Research Ethics Committee of the Irrua Specialist Teaching Hospital, Irrua. Informed written consent was obtained from each participant and from the parents or guardians of participants who were less than 18 years. Participants who were less than 18 years also assented to the study. Confidentiality and anonymity were ensured by not indicating the names of the participants on the questionnaires. Statistical analysis The collected data were analysed using the Statistical Package for Social Sciences (SPSS) version 21. Dependent variables were depression and anxiety. Independent variables were sociodemographic and clinical characteristics. Descriptive statistics were used to summarise socio-demographic and clinical related data and mean with standard deviation for continuous variables. Chi-square (χ2) tests were used to test the association of independent variables with dependent variables. Fisher's exact test was used for cells with expected frequencies < 5. The student's t- test was used to compare means. Binary logistic regression was applied to identify predictors of depression and anxiety that were significant at bivariate analysis. All tests were 2-tailed, and the level of significance was set at a P-value of <0.05.

BMI, body mass index; CT, childhood trauma; CTQ, childhood trauma questionnaire; SAS, self-rating anxiety scale; SD, standard deviation; SDS, self-rating depression scale.**p

Note: Unless otherwise stated, data are presented as means and standard deviations.

Descriptive statistics of the demographic variables of the study participants.

b, unstandardized regression coefficient; s.e., standard error; t, t-value;* p

Mean, standard deviation, and 95% confidence interval of measured variables in the studied groups.

Notes: SCS = Social Curiosity Scale; SCS-G = Subscale Social Curiosity-General; SCS-C = Subscale Social Curiosity-Covert; EC = Epistemic Curiosity Scale; CEI = Curiosity and Exploration Inventory – Trait Form; GFQ = Gossip Function Questionnaire; GFQ-I = Gossip Function Questionnaire-Information Subscale; GFQ-F = Gossip Function Questionnaire-Friendship Subscale; GFQ-If = Gossip Function Questionnaire-Influence Subscale; GFQ-E = Gossip Function Questionnaire-Entertainment Subscale; N = Neuroticism; E = Extraversion; O = Openness. *** ts >3; p

aHigher scores indicate more secondary conditions, more comorbidities and higher pain intensity.bHigher scores indicate better mental health, less participation restrictions, and higher quality of life.cp values from Kruskal-Wallis tests (adjusted for ties).dp values from test for trend across ordered groups.Note: only full cases and unweighted results in this Table.

Note. k =  number of samples; SCJ  =  structured clinical judgment; SD  =  standard deviation. Designer status operationally defined as being an author of the English-language original version of the instrument under investigation.aAt start of follow-up; bViolent and non-violent.

Means and standard deviations (SD) are given. HC = healthy control, SZ = patient with schizophrenia. Ethnicity was defined as described under Methods. WRAT3-RT = Wide Range Achievement Test 3 – Reading Test. Parental SES (socioeconomic status) was classified according to Hollingshead, and handedness determined using the Annett Scale of Hand Preference.asignificantly different between HC and SZ on basis of Chi-Square (p