The Study of Women's Health Across the Nation (SWAN), is a multi-site longitudinal, epidemiologic study designed to examine the health of women during their middle years. The study examines the physical, biological, psychological and social changes during this transitional period. The goal of SWAN's research is to help scientists, health care providers and women learn how mid-life experiences affect health and quality of life during aging. Data were collected about doctor visits, medical conditions, medications, treatments, medical procedures, relationships, smoking, and menopause related information such as age at pre-, peri- and post-menopause, self-attitudes, feelings, and common physical problems associated with menopause. The study began in 1994. Between 2002 and 2004, 2,448 of the 3,302 women that joined SWAN were seen for their sixth follow-up visit. The research centers are located in the following communities: Ypsilanti and Inkster, MI (University of Michigan); Boston, MA (Massachusetts General Hospital); Chicago, IL (Rush Presbyterian-St. Luke's Medical Center); Alameda and Contra Costa County, CA (University of California-Davis and Kaiser Permanente); Los Angeles, CA (University of California-Los Angeles); Hackensack, NJ (Hackensack University Medical Center); and Pittsburgh, PA (University of Pittsburgh). SWAN participants represent five racial/ethnic groups and a variety of backgrounds and cultures. Demographic and background information includes age, language of interview, marital status, household composition, and employment.

Objective: There is a gap in research on gender-based discrimination (GBD) in medical education and practice in Germany. This study therefore examines the extent and forms of GBD among female medical students and physicians in Germany. Causes, consequences and possible interventions of GBD are discussed. Methods: Female medical students (n=235) and female physicians (n=157) from five university hospitals in northern Germany were asked about their personal experiences with GBD in an online survey on self-efficacy expectations and individual perceptions of the “glass ceiling effect” using an open-ended question regarding their own experiences with GBD. The answers were analyzed by content analysis using inductive category formation and relative category frequencies. Results: From both interviewed groups, approximately 75% of each reported having experienced GBD. Their experiences fell into five main categories: sexual harassment with subcategories of verbal and physical, discrimination based on existing/possible motherhood with subcategories of structural and verbal, direct preference for men, direct neglect of women, and derogatory treatment based on gender. Conclusion: The study contributes to filling the aforementioned research gap. At the hospitals studied, GBD is a common phenomenon among both female medical students and physicians, manifesting itself in multiple forms. Transferability of the results beyond the hospitals studied to all of Germany seems plausible. Much is known about the causes, consequences and effective countermeasures against GBD. Those responsible for training and employers in hospitals should fulfill their responsibility by implementing measures from the set of empirically evaluated interventions. Methods Female medical students and physicians from five university hospitals in northern Germany were given an online open question concerning their personal experiences with gernderbased discrimination. The answers were evaluated by qualitative content analysis (Mayring) and by relative frequencies.

The Study of Women's Health Across the Nation (SWAN), is a multi-site longitudinal, epidemiologic study designed to examine the health of women during their middle years. The study examines the physical, biological, psychological and social changes during this transitional period. The goal of SWAN's research is to help scientists, health care providers and women learn how mid-life experiences affect health and quality of life during aging. Data were collected about doctor visits, medical conditions, medications, treatments, medical procedures, relationships, smoking, and menopause related information such as age at pre-, peri- and post-menopause, self-attitudes, feelings, and common physical problems associated with menopause. The study began in 1994. Between 2005 and 2007, 2,255 of the 3,302 women that joined SWAN were seen for their ninth follow-up visit. The research centers are located in the following communities: Ypsilanti and Inkster, MI (University of Michigan); Boston, MA (Massachusetts General Hospital); Chicago, IL (Rush Presbyterian-St. Luke's Medical Center); Alameda and Contra Costa County, CA (University of California-Davis and Kaiser Permanente); Los Angeles, CA (University of California-Los Angeles); Hackensack, NJ (Hackensack University Medical Center); and Pittsburgh, PA (University of Pittsburgh). SWAN participants represent five racial/ethnic groups and a variety of backgrounds and cultures. Though the New Jersey site was still part of the study, data was not collected from this site for the ninth visit. Demographic and background information includes age, language of interview, marital status, household composition, and employment.

About this dataset

Context

The dataset tabulates the population of Medical Lake by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Medical Lake. The dataset can be utilized to understand the population distribution of Medical Lake by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Medical Lake. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Medical Lake.

Key observations

Largest age group (population): Male # 30-34 years (355) | Female # 35-39 years (308). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Medical Lake population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Medical Lake is shown in the following column.
• Population (Female): The female population in the Medical Lake is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Medical Lake for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Medical Lake Population by Gender. You can refer the same here

Perimenopause and menopause can be accompanied by physical and psychological health issues that are known contributors to chronic health conditions such as heart disease, osteoporosis and oral health problems. They may also be associated with hormonal changes that cause symptoms such as night sweats, sleep disturbances, hot flushes, vaginal dryness, urinary frequency, impaired memory, anxiety and depression. Although about 50% of the population will experience menopause, few women are aware of all the health implications associated with perimenopause and menopause. Further, while many women have some understanding about the symptoms associated with perimenopause and menopause, it is unclear where this knowledge is sourced as relatively few women seek advice from a doctor.

This dataset contains de-identified transcripts with 25 women exploring their experiences and knowledge of menopause. This data set also includes the analysis of an online survey of 412 participants. Survey and interview questions are also included.

This dataset contains sensitive information that is not suitable for open publication. To discuss the dataset, please contact Fiona McDermid f.mcdermid@westernsydney.edu.au ORCID 0000-0003-4234-8243.

This table contains 267456 series, with data for years 2000 - 2000 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (199 items: Canada; Health and Community Services Eastern Region; Newfoundland and Labrador (Peer group D); Health and Community Services St. John's Region; Newfoundland and Labrador (Peer group H); Newfoundland and Labrador ...) Age group (14 items: Total; 12 years and over; 12-19 years; 15-19 years; 12-14 years ...) Sex (3 items: Both sexes; Females; Males ...) Contact with medical doctors (4 items: Total population for the variable contact with medical doctors; Contact with medical doctors in past 12 months; Contact with medical doctors; not stated; No contact with medical doctors in past 12 months ...) Characteristics (8 items: Number of persons; Low 95% confidence interval - number of persons; High 95% confidence interval - number of persons; Coefficient of variation for number of persons ...).

This table represents details of Medicaid (coverage for children). Medicaid (coverage for children) is available for many children in working families. Most children who are eligible for Medicaid (coverage for children) do receive their medical care through a health plan, and visit doctors and hospitals that accept that health plan. While ones application is being processed, Medicaid (coverage for children) may provide up to 90 days of retroactive coverage for unpaid medical bills, if eligible during those 90 days

Overview The Human Vital Signs Dataset is a comprehensive collection of key physiological parameters recorded from patients. This dataset is designed to support research in medical diagnostics, patient monitoring, and predictive analytics. It includes both original attributes and derived features to provide a holistic view of patient health.

Attributes Patient ID

Description: A unique identifier assigned to each patient. Type: Integer Example: 1, 2, 3, ... Heart Rate

Description: The number of heartbeats per minute. Type: Integer Range: 60-100 bpm (for this dataset) Example: 72, 85, 90 Respiratory Rate

Description: The number of breaths taken per minute. Type: Integer Range: 12-20 breaths per minute (for this dataset) Example: 16, 18, 15 Timestamp

Description: The exact time at which the vital signs were recorded. Type: Datetime Format: YYYY-MM-DD HH:MM Example: 2023-07-19 10:15:30 Body Temperature

Description: The body temperature measured in degrees Celsius. Type: Float Range: 36.0-37.5°C (for this dataset) Example: 36.7, 37.0, 36.5 Oxygen Saturation

Description: The percentage of oxygen-bound hemoglobin in the blood. Type: Float Range: 95-100% (for this dataset) Example: 98.5, 97.2, 99.1 Systolic Blood Pressure

Description: The pressure in the arteries when the heart beats (systolic pressure). Type: Integer Range: 110-140 mmHg (for this dataset) Example: 120, 130, 115 Diastolic Blood Pressure

Description: The pressure in the arteries when the heart rests between beats (diastolic pressure). Type: Integer Range: 70-90 mmHg (for this dataset) Example: 80, 75, 85 Age

Description: The age of the patient. Type: Integer Range: 18-90 years (for this dataset) Example: 25, 45, 60 Gender

Description: The gender of the patient. Type: Categorical Categories: Male, Female Example: Male, Female Weight (kg)

Description: The weight of the patient in kilograms. Type: Float Range: 50-100 kg (for this dataset) Example: 70.5, 80.3, 65.2 Height (m)

Description: The height of the patient in meters. Type: Float Range: 1.5-2.0 m (for this dataset) Example: 1.75, 1.68, 1.82 Derived Features Derived_HRV (Heart Rate Variability)

Description: A measure of the variation in time between heartbeats. Type: Float Formula: 𝐻 𝑅

𝑉

Standard Deviation of Heart Rate over a Period Mean Heart Rate over the Same Period HRV= Mean Heart Rate over the Same Period Standard Deviation of Heart Rate over a Period ​

Example: 0.10, 0.12, 0.08 Derived_Pulse_Pressure (Pulse Pressure)

Description: The difference between systolic and diastolic blood pressure. Type: Integer Formula: 𝑃

𝑃

Systolic Blood Pressure − Diastolic Blood Pressure PP=Systolic Blood Pressure−Diastolic Blood Pressure Example: 40, 45, 30 Derived_BMI (Body Mass Index)

Description: A measure of body fat based on weight and height. Type: Float Formula: 𝐵 𝑀

𝐼

Weight (kg) ( Height (m) ) 2 BMI= (Height (m)) 2

Weight (kg) ​

Example: 22.8, 25.4, 20.3 Derived_MAP (Mean Arterial Pressure)

Description: An average blood pressure in an individual during a single cardiac cycle. Type: Float Formula: 𝑀 𝐴

𝑃

Diastolic Blood Pressure + 1 3 ( Systolic Blood Pressure − Diastolic Blood Pressure ) MAP=Diastolic Blood Pressure+ 3 1 ​ (Systolic Blood Pressure−Diastolic Blood Pressure) Example: 93.3, 100.0, 88.7 Target Feature Risk Category Description: Classification of patients into "High Risk" or "Low Risk" based on their vital signs. Type: Categorical Categories: High Risk, Low Risk Criteria: High Risk: Any of the following conditions Heart Rate: > 90 bpm or < 60 bpm Respiratory Rate: > 20 breaths per minute or < 12 breaths per minute Body Temperature: > 37.5°C or < 36.0°C Oxygen Saturation: < 95% Systolic Blood Pressure: > 140 mmHg or < 110 mmHg Diastolic Blood Pressure: > 90 mmHg or < 70 mmHg BMI: > 30 or < 18.5 Low Risk: None of the above conditions Example: High Risk, Low Risk This dataset, with a total of 200,000 samples, provides a robust foundation for various machine learning and statistical analysis tasks aimed at understanding and predicting patient health outcomes based on vital signs. The inclusion of both original attributes and derived features enhances the richness and utility of the dataset.

The dataset contains state-wise National Family Health Survey (NFHS) compiled data on various family planning, childbirth, population, medical, health and other parameters which provide statistical indicators data on family profile and health status in India. There are 100+ indicators covered in the survey which broadly fall in the following categories: Health and Wellness, Maternal and Child Health, Family Planning and Reproductive Health, Disease Screening and Prevention, Social and Economic Factors, General Healthcare and Treatment

The different types of health data contained in the dataset include Anaemia among women and children, blood sugar levels and hypertension among men and women, tobacco and alcohol consumption among adults, delivery care and child feeding practices of women, quality of family planning services, screening of cancer among women, marriage and family, maternity care, nutritional status of women, child vaccinations and vitamin A supplementation, treatment of childhood diseases, etc.

Within these categories of health data, the dataset contains indicators data such as births attended by skilled health care professionals and caesarean section, number of children with under and heavy weight, stunted growth, their different vaccations status, male and female sterilization, consumption of iron folic acid among mothers, mother who had antenatal, postnatal, neonatal services, women who are obese and at the risk of weight to hip ratio, educational status among women and children, sanitation, birth and sex ratio, etc.

All of the data is compiled from the NFHS 4th and 5th survey reports. The The NFHS is a collaborative project of the International Institute for Population Sciences(IIPS), aimed at providing health data to strengthen India's health policies and programmes.

There are 100+ indicators covered in the survey which broadly fall in the following categories: Health and Wellness, Maternal and Child Health, Family Planning and Reproductive Health, Disease Screening and Prevention, Social and Economic Factors, General Healthcare and Treatment

This is Dr. Ignaz Semmelweis, a Hungarian physician born in 1818 and active at the Vienna General Hospital. If Dr. Semmelweis looks troubled it's probably because he's thinking about childbed fever: A deadly disease affecting women that just have given birth. He is thinking about it because in the early 1840s at the Vienna General Hospital as many as 10% of the women giving birth die from it. He is thinking about it because he knows the cause of childbed fever: It's the contaminated hands of the doctors delivering the babies. And they won't listen to him and wash their hands!

This data was collected from 45 to 55 years old women who visited from the First Affiliated Hospital of Chongqing Medical University over the period of May 2018 to August 2021 for their annual medical examination.

This data set was collected by Awadji Fabrice Boris and Xue Yuzhou.

There were 1,147 consecutive women recruited.

This data collection was approved by the ethics committee of the First Affiliated Hospital of Chongqing Medical University (No. 2020-23). All the participants gave theirs approval and signed informed consent.

######################### Medical scope, Prompt #1 61K instruction PubMED-Unlabeled ###################################################################Instruction: You are a language model specialized in generating JSON or YAML output. Given an instruction, you should generate a valid JSON or YAML object according to the provided guidelines. Your output should conform to the following JSON format: { "key1": "value1", "key2": "value2", ... } Make sure to adhere to the specified key-value pairs and maintain the correct syntax throughout your response. Your goal is to generate accurate and properly structured JSON or YAML output in response to the given instructions withoutany extra comment. Input: As a doctor that only speak in JSON, provide a "answer", "context and "keywords" about following question: Is naturopathy as effective as conventional therapy for treatment of menopausal symptoms? Output:{"answer" :Naturopathy appears to be an effective alternative for relief of specific menopausal symptoms compared to conventional therapy.,"context" :Although the use of alternative medicine in the United States is increasing, no published studies have documented the effectiveness of naturopathy for treatment of menopausal symptoms compared to women receiving conventional therapy in the clinical setting."keywords" :['Anxiety', 'Cohort Studies', 'Confidence Intervals', 'Estrogen Replacement Therapy', 'Female', 'Health Status', 'Hot Flashes', 'Humans', 'Menopause', 'Middle Aged','Naturopathy', 'Odds Ratio', 'Patient Satisfaction', 'Retrospective Studies', 'Risk Factors', 'Severity of Illness Index', 'Sleep Initiation and Maintenance Disorders','Surveys and Questionnaires', 'Time Factors', 'Urinary Incontinence', 'Vaginal Diseases', 'Washington']} ############################### Medical scope, Prompt #2 symptom-disease dataset 1K instruction ######################################################Instruction: You are a language model specialized in generating JSON or YAML output. Given an instruction, you should generate a valid JSON or YAML object according to theprovided guidelines. Your output should conform to the following JSON format: { "key1": "value1", "key2": "value2", ... } Make sure to adhere to the specified key-value pairs and maintain the correct syntax throughout your response. Your goal is to generate accurate and properly structured JSON or YAML output in response to the given instructions without any extra comment. Input: You are a doctor that only speak in JSON. Do not output that is'nt properly formated JSON. Provide following information about Panic disorder disease. Fill information about "symptom", "reason", "TestsAndProcedures" and "commonMedications" about Panic disorder disease in JSON format. Output: {"symptom" :['Anxiety and nervousness', 'Depression', 'Shortness of breath', 'Depressive or psychotic symptoms', 'Sharp chest pain', 'Dizziness', 'Insomnia', 'Abnormal involuntary movements', 'Chest tightness', 'Palpitations', 'Irregular heartbeat', 'Breathing fast'],"reason" :Panic disorder is an anxiety disorder characterized by recurring severe panic attacks. It may also include significant behavioral changes lasting at least a month and of ongoing worry about the implications or concern about having other attacks. The latter are called anticipatory attacks (DSM-IVR). Panic disorder is not the same as agoraphobia (fear of public places), although many afflicted with panic disorder also suffer from agoraphobia. Panic attacks cannot be predicted, therefore an individual may become stressed, anxious or worried wondering when the next panic attack will occur. Panic disorder may be differentiated as a medical condition, or chemical imbalance. The DSM-IV-TR describes panic disorder and anxiety differently. Whereas anxiety is preceded by chronic stressors which build to reactions of moderate intensity that can last for days, weeks or months, panic attacks are acute events triggered by a sudden, out-of-the-blue cause: duration is short and symptoms are more intense. Panic attacks can occur in children, as well as adults. Panic in young people may be particularly distressing because children tend to have less insight about what is happening, and parents are also likely to experience distress when attacks occur. "TestsAndProcedures" : ['Psychotherapy', 'Mental health counseling', 'Electrocardiogram', 'Depression screen (Depression screening)', 'Toxicology screen', 'Psychological and psychiatric evaluation and therapy']"commonMedications" : ['Lorazepam', 'Alprazolam (Xanax)', 'Clonazepam', 'Paroxetine (Paxil)', 'Venlafaxine (Effexor)', 'Mirtazapine', 'Buspirone (Buspar)', 'Fluvoxamine (Luvox)', 'Imipramine', 'Desvenlafaxine (Pristiq)', 'Clomipramine', 'Acamprosate (Campral)']} ############################### Medical scope, Prompt #3 194K instruction MED-MCQA dataset ######################################################Instruction: You are a language model specialized in generating JSON or YAML output. Given an instruction, you should generate a valid JSON or YAML object according to the provided guidelines. Your output should conform to the following JSON format: { "key1": "value1", "key2": "value2", ... } Make sure to adhere to the specified key-value pairs and maintain the correct syntax throughout your response. Your goal is to generate accurate and properly structured JSON or YAML output in response to the given instructions without any extra comment. Input: You are a medical teacher that only speak in JSON. Do not output that is'nt properly formated JSON. Generate a multichoice question and answer inAnatomy field and Urinary tract topic with following format: { "question": "expresion" , "OPA": "expresion", "OPB" : "expresion", "OPC": "expresion","OPD": "expresion", "Answer": "3"}. Output: { "question": Chronic urethral obstruction due to benign prismatic hyperplasia can lead to the following change in kidney parenchyma, "opa": Hyperplasia, "opb": Hyperophy, "opc": Atrophy, "opd": Dyplasia, "Answer": 2. ############################### Finance scope, Prompt #4 38K instruction ######################################################Instruction: You are a language model specialized in generating JSON or YAML output. Given an instruction, you should generate a valid JSON or YAML object according to the provided guidelines. Your output should conform to the following JSON format: { "key1": "value1", "key2": "value2", ... } Make sure to adhere to the specified key-value pairs and maintain the correct syntax throughout your response. Your goal is to generate accurate and properly structured JSON or YAML output in response to the given instructions without any extra comment. Input: You are a financial news analyzer that only speaks in JSON. Do not output that isn't properly formatted JSON. Analyze this news title "Here Why Bitcoin is Likely to See Historic Volatility in the Next Few Days". Provide sentiment as a probability distribution also indicate target market and related asset to the news title in JSON format. Output:{"sentiment": {"positive": 0.4463904500007629, "negative": 0.06607405841350555,"neutral":0.4875355064868927}, "market": "cryptocurrency" , "relatedAsset": ['BTC/USDT']}.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Medical Lake. The dataset can be utilized to gain insights into gender-based income distribution within the Medical Lake population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Medical Lake, among individuals aged 15 years and older with income, there were 1,701 men and 1,952 women in the workforce. Among them, 903 men were engaged in full-time, year-round employment, while 918 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 4.54% fell within the income range of under $24,999, while 3.38% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 7.42% of men in full-time roles earned incomes exceeding $100,000, while 5.01% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Medical Lake median household income by race. You can refer the same here

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Medical Lake. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Medical Lake, the median income for all workers aged 15 years and older, regardless of work hours, was $45,564 for males and $37,398 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 18% between the median incomes of males and females in Medical Lake. With women, regardless of work hours, earning 82 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Medical Lake.

- Full-time workers, aged 15 years and older: In Medical Lake, among full-time, year-round workers aged 15 years and older, males earned a median income of $75,619, while females earned $44,158, leading to a 42% gender pay gap among full-time workers. This illustrates that women earn 58 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

Remarkably, across all roles, including non-full-time employment, women displayed a lower gender pay gap percentage. This indicates that Medical Lake offers better opportunities for women in non-full-time positions.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Medical Lake median household income by race. You can refer the same here

This dataset summarizes the number of women on Medical Assistance (MA) ages 12 to 55 years old with a delivery and indicates how many of those women were diagnosed with Opioid Use Disorder (OUD) during their pregnancy. Delivery includes live birth or stillbirth. Data collection started in 2016 and will be updated quarterly as data becomes available.

Context

Content

A few years ago, the United States District Court of Houston had a case that arises under Title VII of the Civil Rights Act of 1964, 42 U.S.C. 200e et seq. The plaintiffs in this case were all female doctors at Houston College of Medicine who claimed that the College has engaged in a pattern and practice of discrimination against women in giving promotions and setting salaries. The Lead plaintiff in this action, a pediatrician and an assistant professor, was denied for promotion at the College. The plaintiffs had presented a set of data to show that female faculty at the school were less likely to be full professors, more likely to be assistant professors, and earn less money than men, on average.

1 Dept 1=Biochemistry/Molecular Biology 2=Physiology 3=Genetics 4=Pediatrics 5=Medicine 6=Surgery

2 Gender 1=Male, 0=Female

3 Clin 1=Primarily clinical emphasis, 0=Primarily research emphasis

4 Cert 1=Board certified, 0=not certified

5 Prate Publication rate (# publications on cv)/(# years between CV date and MD date)

6 Exper # years since obtaining MD

7 Rank 1=Assistant, 2=Associate, 3=Full professor (a proxy for productivity)

8 Sal94 Salary in academic year 1994

9 Sal95 Salary after increment to 1994

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

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This repository contains the raw data of Polish adaptation of Physician’s Trust in the Patient Scale (PTPS) – psychometric properties and validation. The purpose of the study was to adapt into Polish the Physician's Trust in the Patient Scale (PTPS) (Thom et al., 2011) and to determine its internal structure and psychometric properties: reliability and theoretical, criterion, convergent, and discriminant validity. The data was gathered by the survey in the form of a questionnaire conducted online with the use of Qualtrics platform. The method of recruiting the respondents: invitations were sent by email directly to medical facilities, hospitals, and outpatient clinics, as well as to medical universities in Poland. 307 medical doctors representing 51 various medical specialties participated in the study. This number included: 168 women, 138 men, and one person not identifying with any of the above - mentioned genders. Participants came from 26 various cities in Poland. In order to avoid the possibility of identifying the participants, we decided to remove from the dataset the following sociodemographic data: gender, residence, marital status, information about having children, workplace, employment duration and length of professional experience.

The dataset contains all the other data that allows to replicate the results and carry out all the calculations that we have implemented in our original research. This includes the results of the following measures: 1) Physician's Trust in the Patient Scale (referred to as PTPS) (Thom et al., 2011); 2) The Disposition to Trust & Trusting Beliefs Measure (referred to as DtT and TBM) (McKnight et al., 2002); 3) General Trust Scale (referred to as GTS) (Yamagishi & Yamagishi, 1994); 4) Oldenburg Burnout Inventory (referred to as OLBI) (Demerouti & Bakker, 2007); 5) Self-efficacy subscale from the Copenhagen Psychosocial Questionnaire COPSOQ II (referred to as S_E) (Pejtersen et al., 2010); 6) Job Satisfaction subscale from the Copenhagen Psychosocial Questionnaire COPSOQ II (referred to as JS) (Pejtersen et al., 2010); 7) Ten-Item Personality Inventory (referred to as TIPI) (Gosling et al., 2003). All measures used in the study were previously validated Polish versions with satisfying psychometric properties.

The variables signed with R in the end, means that they are reversed, accordingly to the appropriate measure key. The numbers of variables are in accordance with the number of questions in the given tools. The missing data is signed with the 9 (all items), 99 (for medical specialty), or 999 (for age).

The repository contains also the PDF file (Appendix A.) with the legend of the numbers representing particular medical specialties (the list is in accordance with the specialties currently operating in Poland).

This dataset contains genome-wide single nucleotide polymorphism (SNP) genotype data generated from 81 individuals (78 women and 3 men of Aymara-Quecha and Uru ethnicities) from the Bolivian Altiplano and 32 women from the Argentinean Puna (Atacameño-Kolla ethnicity). DNA was extracted from whole blood samples or buccal cells with EZNA Blood DNA Mini kit (Omega Bio-teck, USA) or Qiagen Blood Mini kit (Qiagen, Germany). Genome-wide genotyping was performed at the SNP&Seq Technology Platform in Uppsala (Sweden) on the Illumina Infinium Omni5Exome and on the Illumina Infinium Omni5M bead chips for the Bolivian and Argentinean study groups, respectively. The data was aligned to the human reference genome build, version 37 (hg19).

This metadata record contains information of 162 IDAT files generated from the Illumina Infinium Omni5Exome and on the Illumina Infinium Omni5M arrays, and 3 PLINK files (bim, bam and fam). The data takes 10GB of storage.

Terms for access:

· The genome-wideSNP genotype dataset is only to be used for research that is seeking to advance the understanding of the human adaptation to extreme environments.

· The data should not be used for other purposes, i.e. investigating the genetic signatures that may lead to identification of a person.

· Not to use this data or any part thereof for the creation of products or services for sale or to sale the data or parts of the data or to use the data for any commercial purpose.

· Do not to transfer or disclose the data, in whole or part, or any identifiable material derived from the data, to third parties or persons not directly involved in the research.

· Preserve, at all times, the confidentiality of information and data pertaining to sample donors. In particular, not to use, or attempt to use the data to compromise or otherwise infringe the confidentiality of information of the sample donors and their right to privacy.

· Not to use the information included in the data to identify the data subjects nor to contact them under any circumstances.

· Any work based in whole or part on the data shall acknowledge the published paper from which the data derives.

The 2008 National Demographic and Health Survey (2008 NDHS) is a nationally representative survey of 13,594 women age 15-49 from 12,469 households successfully interviewed, covering 794 enumeration areas (clusters) throughout the Philippines. This survey is the ninth in a series of demographic and health surveys conducted to assess the demographic and health situation in the country. The survey obtained detailed information on fertility levels, marriage, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and young children, childhood mortality, maternal and child health, and knowledge and attitudes regarding HIV/AIDS and tuberculosis. Also, for the first time, the Philippines NDHS gathered information on violence against women. The 2008 NDHS was conducted by the Philippine National Statistics Office (NSO). Technical assistance was provided by ICF Macro through the MEASURE DHS program. Funding for the survey was mainly provided by the Government of the Philippines. Financial support for some preparatory and processing phases of the survey was provided by the U.S. Agency for International Development (USAID). Like previous Demographic and Health Surveys (DHS) conducted in the Philippines, the 2008 National Demographic and Health Survey (NDHS) was primarily designed to provide information on population, family planning, and health to be used in evaluating and designing policies, programs, and strategies for improving health and family planning services in the country. The 2008 NDHS also included questions on domestic violence. Specifically, the 2008 NDHS had the following objectives: Collect data at the national level that will allow the estimation of demographic rates, particularly, fertility rates by urban-rural residence and region, and under-five mortality rates at the national level. Analyze the direct and indirect factors which determine the levels and patterns of fertility. Measure the level of contraceptive knowledge and practice by method, urban-rural residence, and region. Collect data on family health: immunizations, prenatal and postnatal checkups, assistance at delivery, breastfeeding, and prevalence and treatment of diarrhea, fever, and acute respiratory infections among children under five years. Collect data on environmental health, utilization of health facilities, prevalence of common noncommunicable and infectious diseases, and membership in health insurance plans. Collect data on awareness of tuberculosis. Determine women's knowledge about HIV/AIDS and access to HIV testing. Determine the extent of violence against women. MAIN RESULTS FERTILITY Fertility Levels and Trends. There has been a steady decline in fertility in the Philippines in the past 36 years. From 6.0 children per woman in 1970, the total fertility rate (TFR) in the Philippines declined to 3.3 children per woman in 2006. The current fertility level in the country is relatively high compared with other countries in Southeast Asia, such as Thailand, Singapore and Indonesia, where the TFR is below 2 children per woman. Fertility Differentials. Fertility varies substantially across subgroups of women. Urban women have, on average, 2.8 children compared with 3.8 children per woman in rural areas. The level of fertility has a negative relationship with education; the fertility rate of women who have attended college (2.3 children per woman) is about half that of women who have been to elementary school (4.5 children per woman). Fertility also decreases with household wealth: women in wealthier households have fewer children than those in poorer households. FAMILY PLANNING Knowledge of Contraception. Knowledge of family planning is universal in the Philippines- almost all women know at least one method of fam-ily planning. At least 90 percent of currently married women have heard of the pill, male condoms, injectables, and female sterilization, while 87 percent know about the IUD and 68 percent know about male sterilization. On average, currently married women know eight methods of family planning. Unmet Need for Family Planning. Unmet need for family planning is defined as the percentage of currently married women who either do not want any more children or want to wait before having their next birth, but are not using any method of family planning. The 2008 NDHS data show that the total unmet need for family planning in the Philippines is 22 percent, of which 13 percent is limiting and 9 percent is for spacing. The level of unmet need has increased from 17 percent in 2003. Overall, the total demand for family planning in the Philippines is 73 percent, of which 69 percent has been satisfied. If all of need were satisfied, a contraceptive prevalence rate of about 73 percent could, theoretically, be expected. Comparison with the 2003 NDHS indicates that the percentage of demand satisfied has declined from 75 percent. MATERNAL HEALTH Antenatal Care. Nine in ten Filipino mothers received some antenatal care (ANC) from a medical professional, either a nurse or midwife (52 percent) or a doctor (39 percent). Most women have at least four antenatal care visits. More than half (54 percent) of women had an antenatal care visit during the first trimester of pregnancy, as recommended. While more than 90 percent of women who received antenatal care had their blood pressure monitored and weight measured, only 54 percent had their urine sample taken and 47 percent had their blood sample taken. About seven in ten women were informed of pregnancy complications. Three in four births in the Philippines are protected against neonatal tetanus. Delivery and Postnatal Care. Only 44 percent of births in the Philippines occur in health facilities-27 percent in a public facility and 18 percent in a private facility. More than half (56 percent) of births are still delivered at home. Sixty-two percent of births are assisted by a health professional-35 percent by a doctor and 27 percent by a midwife or nurse. Thirty-six percent are assisted by a traditional birth attendant or hilot. About 10 percent of births are delivered by C-section. The Department of Health (DOH) recommends that mothers receive a postpartum check within 48 hours of delivery. A majority of women (77 percent) had a postnatal checkup within two days of delivery; 14 percent had a postnatal checkup 3 to 41 days after delivery. CHILD HEALTH Childhood Mortality. Childhood mortality continues to decline in the Philippines. Currently, about one in every 30 children in the Philippines dies before his or her fifth birthday. The infant mortality rate for the five years before the survey (roughly 2004-2008) is 25 deaths per 1,000 live births and the under-five mortality rate is 34 deaths per 1,000 live births. This is lower than the rates of 29 and 40 reported in 2003, respectively. The neonatal mortality rate, representing death in the first month of life, is 16 deaths per 1,000 live births. Under-five mortality decreases as household wealth increases; children from the poorest families are three times more likely to die before the age of five as those from the wealthiest families. There is a strong association between under-five mortality and mother's education. It ranges from 47 deaths per 1,000 live births among children of women with elementary education to 18 deaths per 1,000 live births among children of women who attended college. As in the 2003 NDHS, the highest level of under-five mortality is observed in ARMM (94 deaths per 1,000 live births), while the lowest is observed in NCR (24 deaths per 1,000 live births). NUTRITION Breastfeeding Practices. Eighty-eight percent of children born in the Philippines are breastfed. There has been no change in this practice since 1993. In addition, the median durations of any breastfeeding and of exclusive breastfeeding have remained at 14 months and less than one month, respectively. Although it is recommended that infants should not be given anything other than breast milk until six months of age, only one-third of Filipino children under six months are exclusively breastfed. Complementary foods should be introduced when a child is six months old to reduce the risk of malnutrition. More than half of children ages 6-9 months are eating complementary foods in addition to being breastfed. The Infant and Young Child Feeding (IYCF) guidelines contain specific recommendations for the number of times that young children in various age groups should be fed each day as well as the number of food groups from which they should be fed. NDHS data indicate that just over half of children age 6-23 months (55 percent) were fed according to the IYCF guidelines. HIV/AIDS Awareness of HIV/AIDS. While over 94 percent of women have heard of AIDS, only 53 percent know the two major methods for preventing transmission of HIV (using condoms and limiting sex to one uninfected partner). Only 45 percent of young women age 15-49 know these two methods for preventing HIV transmission. Knowledge of prevention methods is higher in urban areas than in rural areas and increases dramatically with education and wealth. For example, only 16 percent of women with no education know that using condoms limits the risk of HIV infection compared with 69 percent of those who have attended college. TUBERCULOSIS Knowledge of TB. While awareness of tuberculosis (TB) is high, knowledge of its causes and symptoms is less common. Only 1 in 4 women know that TB is caused by microbes, germs or bacteria. Instead, respondents tend to say that TB is caused by smoking or drinking alcohol, or that it is inherited. Symptoms associated with TB are better recognized. Over half of the respondents cited coughing, while 39 percent mentioned weight loss, 35 percent mentioned blood in sputum, and 30 percent cited coughing with sputum. WOMEN'S STATUS Women's Status and Employment.

Western Cape antenatal ART coverage (%) amongst women living with HIV by dataset (2011–2020).