Connecticut Nurses Census 1917

The Connecticut Nurses Census is a part of State Archives https://cslarchives.ctstatelibrary.org/repositories/2/resources/443">Record Group 029: Records of the Military Census Department. The census forms may give basic details such as birthplace, age, marital status, maiden name, and current residence, as well as more specific information such as the name of the nursing school attended, medical specialty, and year of licensure. This census included the registration of both female and male nurses.

This index includes the name, birthplace, age, current residence, form number and box number. If a field is left blank, it is because the person who submitted the form did not answer that question (e.g. age, anybody!) People may request a copy of a census form by contacting us by telephone (860) 757-6580 or email. Please include the name of the individual and form number.

This dataset, drawn from the WHO Policy Survey 2023, identifies whether a country recognises midwifery as a standalone occupational group separate from nursing. Distinct recognition is foundational to professional autonomy, regulation, and education. This indicator supports analysis of national frameworks that enable or limit the visibility and growth of the midwifery profession.Data Source:WHO Policy Survey 2023: https://www.who.int/publications/i/item/9789240100176Data Dictionary: The data is collated with the following columns:Column headingContent of this columnPossible valuesRefNumerical counter for each row of data, for ease of identification1+CountryShort name for the country195 countries in total – all 194 WHO member states plus PalestineISO3Three-digit alphabetical codes International Standard ISO 3166-1 assigned by the International Organization for Standardization (ISO). e.g. AFG (Afghanistan)ISO22 letter identifier code for the countrye.g. AF (Afghanistan)ICM_regionICM Region for countryAFR (Africa), AMR (Americas), EMR (Eastern Mediterranean), EUR (Europe), SEAR (South east Asia) or WPR (Western Pacific)CodeUnique project code for each indicator:GGTXXnnnGG=data group e.g. OU for outcomeT = N for novice or E for ExpertXX = identifier number 00 to 30nnn = identifier name eg mmre.g. OUN01sbafor Outcome Novice Indicator 01 skilled birth attendance Short_nameIndicator namee.g. maternal mortality ratioDescriptionText description of the indicator to be used on websitee.g. Maternal mortality ratio (maternal deaths per 100,000 live births)Value_typeDescribes the indicator typeNumeric: decimal numberPercentage: value between 0 & 100Text: value from list of text optionsY/N: yes or noValue_categoryExpect this to be ‘total’ for all indicators for Phase 1, but this could allow future disaggregation, e.g. male/female; urban/ruraltotalYearThe year that the indicator value was reported. For most indicators, we will only report if 2014 or more recente.g. 2020Latest_Value‘LATEST’ if this is the most recent reported value for the indicator since 2014, otherwise ‘No’. Useful for indicators with time trend data.LATEST or NOValueIndicator valuee.g. 99.8. NB Some indicators are calculated to several decimal places. We present the value to the number of decimal places that should be displayed on the Hub.SourceFor Caesarean birth rate [OUN13cbr] ONLY, this column indicates the source of the data, either OECD when reported, or UNICEF otherwise.OECD or UNICEFTargetHow does the latest value compare with Global guidelines / targets?meets targetdoes not meet targetmeets global standarddoes not meet global standardRankGlobal rank for indicator, i.e. the country with the best global score for this indicator will have rank = 1, next = 2, etc. This ranking is only appropriate for a few indicators, others will show ‘na’1-195Rank out ofThe total number of countries who have reported a value for this indicator. Ranking scores will only go as high as this number.Up to 195TrendIf historic data is available, an indication of the change over time. If there is a global target, then the trend is either getting better, static or getting worse. For mmr [OUN04mmr] and nmr [OUN05nmr] the average annual rate of reduction (arr) between 2016 and latest value is used to determine the trend:arr <-1.0 = getting worsearr >=-1.0 AND <=1.0 = staticarr >1.0 = getting betterFor other indicators, the trend is estimated by comparing the average of the last three years with the average ten years ago:decreasing if now < 95% 10 yrs agoincreasing if now > 105% 10 yrs agostatic otherwiseincreasingdecreasing Or, if there is a global target: getting better,static,getting worseNotesClarification comments, when necessary LongitudeFor use with mapping LatitudeFor use with mapping DateDate data uploaded to the Hubthe following codes are also possible values:not reported does not apply don’t knowThis is one of many datasets featured on the Midwives’ Data Hub, a digital platform designed to strengthen midwifery and advocate for better maternal and newborn health services.

Parkinson’s disease is an often-debilitating progressive neurological condition leading to loss of motor control. This dataset contains kinematic sensor data from two groups: one containing 15 patients with Parkinson’s disease, and a control group of 19 participants without any known neurological condition. Participant ages ranged from 40–85, 21 were male, and 13 were female. The participants wore five 9-axis Inertial Measurement Units (IMUs) – one on each upper arm, each lower arm, and on their head. They were asked to perform a calibration pose, followed by three activities: making toast, putting on a cardigan, and unlocking and opening a door, with each activity repeated three times. The IMUs recorded time-series acceleration and orientation data from the moment where the participant was instructed to begin the activity (inception of the idea to act), through to the activity’s completion. This dataset is planned for use in intent-sensing studies for assistive device control but is also applicable for activity recognition. Methods Data in this study came from a set of 34 volunteers, 15 of whom had Parkinson’s disease and 19 of whom did not. 21 of the participants were male and 13 female, with ages ranging from 40 to 85. All volunteers signed an informed consent form and ethical approval for the study was obtained from the NRES Committee South West (REC reference 13/SW/0287). The data collection was performed by research nurses, who supervised the participants throughout the process. Initially, the participants stood in a calibration pose, with their arms by their sides, with this data recorded for standardization. The participants then performed three activities of daily living (ADLs) – unlocking and opening a door, buttoning and unbuttoning a cardigan, and making toast. Each activity was repeated three times, without a break. Data recording began at the moment the participant was instructed to begin the task. The participants each wore five Xsens IMU three-axis nine-channel IMUs (MTx, Xsens Technologies B. V., Enschede, Netherlands). These were secured to the participants' lower and upper arms (both left and right), and to their head. See the supplementary figure for a photograph of this. During the activities, the participant was engaged in conversation by the supervising research nurses but were asked not to talk about the activity they were performing. This engagement was aimed at making the motor behaviour more natural and to better represent activities of daily living in which cognitive loading is increased due to the application of multitasking. Each IMU provided magnetometer, gyroscope, and accelerometer data, along with a 3x3 rotation matrix provided by the XSens software.

The Synthetic Diarrhea Etiology Dataset is a comprehensive, anonymized dataset created for educational and research purposes. It simulates pediatric clinical records related to diarrhea episodes, collected across different locations and times. The dataset supports analysis of symptom progression, clinical signs, treatment practices, and socio-environmental conditions related to diarrhea etiology and outcomes.

Dataset Features

• Study ID: Unique identifier for each patient.
• Admit Date & Time: Timestamp of hospital admission.
• Gender: Biological sex of the patient (Male/Female).
• Age (Months): Patient age in months.
• Diarrhea Hours/Days/Episodes: Duration and frequency of diarrhea symptoms.
• Nurse Name & Exam Time: Attending nurse and examination timestamp.
• Vital Signs: Temperature, respiration rate.
• Stool & Symptom Reporting: Observations and reports of blood in stool, fever, vomiting, and breastfeeding status.
• Anthropometrics: Height and mid-upper arm circumference (MUAC).
• Seasonality: Time of year when case occurred.
• Patient Status: Classification codes for patient condition.
• % Viral: Estimated viral etiology percentage.
• Socioeconomic Indicators: Parent education levels, household income, number of people at home, shared pot use.
• Medications: Usage and type of medication administered.
• Stool Sample Collection: Time, date, and location of sample collection.

Distribution

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Usage

This dataset can be used for:

• Epidemiological Research: Explore the relationships between demographic, environmental, and clinical factors affecting diarrhea in children.
• Predictive Modelling: Build models to predict symptom severity, etiology (viral vs. bacterial), or recovery time.
• Clinical Insights: Analyse symptom presentation, vital signs, and treatment effectiveness.
• Educational Purposes: Serve as a realistic dataset for students in global health, pediatric epidemiology, and data science.

Coverage

This synthetic dataset is fully anonymized and adheres to privacy standards. It is designed to support robust statistical and machine learning analysis without compromising personal data.

License

CC0 (Public Domain)

Who Can Use It

• Public Health Researchers: To study pediatric diarrhea patterns and intervention impacts.
• Medical and Clinical Practitioners: To understand how vital signs and symptoms relate to diagnosis and treatment.
• Data Scientists and Machine Learning Experts: To experiment with clinical classification and prediction tasks.
• Students and Educators: As a case study in health informatics, biostatistics, and data-driven public health strategies.

This dataset reports the number of midwives per 10,000 population, based on data from the WHO National Health Workforce Accounts (NHWA) platform. It provides a standardised measure of workforce density, reflecting the availability of midwifery services in relation to population size. This indicator is essential for assessing health system capacity, identifying gaps in coverage, and informing policies aimed at equitable access to skilled midwifery care worldwide.Number of midwives (Midwifery Professionals + Midwifery Associate Professionals + Nurse-midwife professionals + Nurse-midwife associate professionals) per 10,000 population. Note: Data doesn't include nurse-midwives.Data Source: WHO national health workforce reporting systems: https://apps.who.int/nhwaportal/Data Dictionary:The data is collated with the following columns:Column headingContent of this columnPossible valuesRefNumerical counter for each row of data, for ease of identification1+CountryShort name for the country195 countries in total – all 194 WHO member states plus PalestineISO3Three-digit alphabetical codes International Standard ISO 3166-1 assigned by the International Organization for Standardization (ISO). e.g. AFG (Afghanistan)ISO22 letter identifier code for the countrye.g. AF (Afghanistan)ICM_regionICM Region for countryAFR (Africa), AMR (Americas), EMR (Eastern Mediterranean), EUR (Europe), SEAR (South east Asia) or WPR (Western Pacific)CodeUnique project code for each indicator:GGTXXnnnGG=data group e.g. OU for outcomeT = N for novice or E for ExpertXX = identifier number 00 to 30nnn = identifier name eg mmre.g. OUN01sbafor Outcome Novice Indicator 01 skilled birth attendance Short_nameIndicator namee.g. maternal mortality ratioDescriptionText description of the indicator to be used on websitee.g. Maternal mortality ratio (maternal deaths per 100,000 live births)Value_typeDescribes the indicator typeNumeric: decimal numberPercentage: value between 0 & 100Text: value from list of text optionsY/N: yes or noValue_categoryExpect this to be ‘total’ for all indicators for Phase 1, but this could allow future disaggregation, e.g. male/female; urban/ruraltotalYearThe year that the indicator value was reported. For most indicators, we will only report if 2014 or more recente.g. 2020Latest_Value‘LATEST’ if this is the most recent reported value for the indicator since 2014, otherwise ‘No’. Useful for indicators with time trend data.LATEST or NOValueIndicator valuee.g. 99.8. NB Some indicators are calculated to several decimal places. We present the value to the number of decimal places that should be displayed on the Hub.SourceFor Caesarean birth rate [OUN13cbr] ONLY, this column indicates the source of the data, either OECD when reported, or UNICEF otherwise.OECD or UNICEFTargetHow does the latest value compare with Global guidelines / targets?meets targetdoes not meet targetmeets global standarddoes not meet global standardRankGlobal rank for indicator, i.e. the country with the best global score for this indicator will have rank = 1, next = 2, etc. This ranking is only appropriate for a few indicators, others will show ‘na’1-195Rank out ofThe total number of countries who have reported a value for this indicator. Ranking scores will only go as high as this number.Up to 195TrendIf historic data is available, an indication of the change over time. If there is a global target, then the trend is either getting better, static or getting worse. For mmr [OUN04mmr] and nmr [OUN05nmr] the average annual rate of reduction (arr) between 2016 and latest value is used to determine the trend:arr <-1.0 = getting worsearr >=-1.0 AND <=1.0 = staticarr >1.0 = getting betterFor other indicators, the trend is estimated by comparing the average of the last three years with the average ten years ago:decreasing if now < 95% 10 yrs agoincreasing if now > 105% 10 yrs agostatic otherwiseincreasingdecreasing Or, if there is a global target: getting better,static,getting worseNotesClarification comments, when necessary LongitudeFor use with mapping LatitudeFor use with mapping DateDate data uploaded to the Hubthe following codes are also possible values:not reported does not apply don’t knowThis is one of many datasets featured on the Midwives’ Data Hub, a digital platform designed to strengthen midwifery and advocate for better maternal and newborn health services.

The 2001 Nepal Demographic and Health Survey (NDHS) is a nationally representative survey of 8,726 women age 15-49 and 2,261 men age 15-59. This Survey is the sixth in a series of national-level population and health surveys conducted in Nepal. It is the second nationally representative comprehensive survey conducted as part of the global Demographic and Health Survey (DHS) program, the first being the 1996 Nepal Family Health Survey (NFHS). The 2001 NDHS is the first in the history of demographic and health surveys conducted in Nepal that included a male sample. The 2001 NDHS was carried out under the aegis of the Family Health Division of the Department of Health Services, Ministry of Health, and was implemented by New ERA, a local research organization, which also conducted the 1996 NFHS. ORC Macro provided technical support through its MEASURE DHS+ project. The survey was funded by the United States Agency for International Development (USAID) through its mission in Nepal. The principal objective of the 2001 NDHS is to provide current and reliable data on fertility and family planning, infant and child mortality, children's and women's nutritional status, the utilization of maternal and child health services, and knowledge of HIV/AIDS. This information is essential for informed policy decisions, planning, monitoring, and evaluation of programs on health in general and reproductive health in particular at both the national and regional levels. A long-term objective of the survey is to strengthen the technical capacity of the Family Health Division of the Ministry of Health to plan, conduct, process, and analyze data from complex national population and health surveys. The 2001 NDHS data is comparable to data collected in the 1996 NFHS and similar to survey data conducted in other developing countries. This allows for temporal and spatial comparisons of demographic health information. The 2001 NDHS also adds to the vast and growing international database on demographic and health variables. The inclusion of data on men adds to the richness of this data. MAIN RESULTS FERTILITY Comparison of data from the 2001 NDHS with earlier surveys conducted in Nepal indicates that fertility has declined steadily from 5.1 births per woman in 1984-1986 to 4.1 births per woman in 1998-2000. Further evidence of recent fertility decline is obtained from the pregnancy history information collected in the 2001 NDHS. There has been an 18 percent decline in fertility among women below age 30, from 3.6 births per woman during the period 15-19 years before the survey to 2.9 births per woman during the period 0-4 years before the survey, with the largest decline in fertility (14 percent) occurring between 5-9 and 0-4 years before the survey. Differences by place of residence are marked, with rural women having more than twice as many children (4.4) as urban women (2.1). Fertility is highest in the mountains (4.8 births per woman), with little difference in fertility between the hills (4.0 births per woman) and the terai (4.1 births per woman). Education is strongly related to fertility, with uneducated women having more than twice as many children (4.8) as women with at least some secondary education (2.3). Data from the national censuses and the 2001 NDHS indicate that the proportion never married among women and men below age 25 has increased gradually over time. Only one in four women age 15-19 was not married in 1961, compared with three in five women in 2001. Similarly in 1961, 5 percent of women age 20-24 had never married, compared with more than three times as many in the same age group five decades later. A similar pattern of decline in nuptiality is observed among men as well, with a proportionately larger change again observed among the youngest age group. FAMILY PLANNING Findings from the 2001 NDHS show that knowledge of family planning is nearly universal among Nepalese women and men. Knowledge of modern methods is generally much higher than knowledge of traditional methods, with women and men being most familiar with female and male sterilization. The mass media are important sources of information on family planning. Three in five women and seven in ten men have heard or seen messages about family planning on the radio, on television, or in print media. The majority of couples approve of family planning. Discussion of family planning between spouses continues to be relatively uncommon, with only two in five women and one in two men who know of a contraceptive method having discussed family planning with their spouse in the year before the survey. The contraceptive prevalence rate among currently married Nepalese women is 39 percent. There has been an impressive increase in the use of contraception in Nepal over the last 25 years, with the increase in current use highest in the most recent five-year period?a 35 percent increase between 1996 and 2001. During this period, the use of modern methods increased from 26 percent to 35 percent among currently married women, with the increase largely attributed to the increase in the use of injectables and female sterilization. There has been a twofold increase in the share of temporary methods over all modern methods in the last decade and a decline in the share of permanent methods overall. Nevertheless, there continues to be a marked discrepancy between ever use of contraception and current use. One in two currently married women has ever used a modern method of family planning, compared with only one in three who is currently using. Similarly, three-fifths of currently married men have ever used a modern, method compared with slightly more than two-fifths who are current users. The most widely used modern method is female sterilization (15 percent among currently married women), followed by injectables (8 percent) and male sterilization (6 percent). Currently married men report a higher use of contraceptives with the largest male/female discrepancy in the use of condoms, with twice as many currently married men as currently married women reporting using condoms (6 percent versus 3 percent). Men also report a much higher use of female sterilization (17 percent) and injectables (10 percent). CHILD HEALTH One in every 11 children born in Nepal dies before reaching age five. Slightly more than two in three under-five deaths occur in the first year of life?infant mortality is 64 deaths per 1,000 live births, and child mortality is 29 deaths per 1,000 live births. During infancy, the risk of neonatal deaths (39 per 1,000) is one and a half times as high as the risk of postneonatal death (26 per 1,000). According to data collected in the 2001 NDHS, mortality levels have declined rapidly since the early 1980s. Under-five mortality in the five years before the survey is 58 percent of what it was 10-14 years before the survey. Comparable data for child mortality (50 percent) and infant mortality (60 percent) indicate that the pace of decline is somewhat faster for child mortality than for infant mortality. The corresponding figures for neonatal and postneonatal mortality are 61 percent and 58 percent, respectively. This decline in childhood mortality levels is confirmed by data from other sources. Sixty percent of children are fully vaccinated by 12 months of age, 83 percent have received the BCG vaccination, and 64 percent have been vaccinated against measles. Coverage for the first dose of DPT is 83 percent, but this drops to 77 percent for the second dose and further to 71 percent for the third dose. Polio coverage is much higher at 97 percent for the first dose, 96 percent for the second dose, and 90 percent for the third dose. The percentage of children age 12-23 months fully immunized by age one has increased in the last five years by 67 percent. The corresponding increases in the third dose of DPT and polio are 39 percent and 87 percent, respectively, while BCG coverage increased by 13 percent and measles vaccination increased by 41 percent. The much higher increase in polio coverage was primarily due to the success of the intensive national immunization day campaigns and other polio eradication activities. MATERNAL HEALTH One in two pregnant women receives antenatal care in Nepal, with 28 percent receiving care from a doctor or nurse, midwife, or auxiliary nurse midwife. In addition, 11 percent of women receive antenatal care from a health assistant or auxiliary health worker, 3 percent receive care from a maternal and child health worker, and 6 percent receive care from a village health worker. Most Nepalese women who receive antenatal care get it at a relatively late stage in their pregnancy and do not make the minimum recommended number of antenatal visits. Only one in seven women (14 percent) makes four or more visits during their entire pregnancy, while 16 percent of women report that their first visit occurred at less than four months of pregnancy. About half of mothers who receive antenatal care report that they were informed about the signs of pregnancy complications, while three in five women report that their blood pressure was measured as part of their routine antenatal care checkup. Forty-five percent of women receive two or more doses of tetanus toxoid injections during their most recent pregnancy. Institutional deliveries are not common in Nepal. Less than one in ten births in the five years preceding the survey took place in a health facility. Thirteen percent of births were attended at delivery by a medical professional, with only 8 percent of births attended by a doctor and 3 percent attended by a nurse, midwife, or auxiliary nurse midwife. Nearly one in four births was attended by a traditional birth attendant. Safe delivery kits were used in 9 percent of births delivered at home. Postnatal care, an important

vaccination against COVID-19 From the start of the vaccination campaign, the health authorities were provided with information allowing daily monitoring of the progress and deployment of the campaign on the territory. These were collected from institutions for the elderly and vaccination centres and were transmitted by the Regional Health Agencies. At the same time, Health Insurance has developed the Vaccine Covid Information System (VAC-SI), which is now fully operational after analysing the completeness and completeness of the data. The Vaccine Covid information system is powered by health professionals carrying out vaccinations. Based on the use of these data, Santé Publique France publishes the vaccination coverage indicators in open data. ### what data? Vaccine coverage of healthcare professionals in a health facility vaccinated against COVID-19 by at least one dose or completely vaccinated by injection date. Vaccination coverage is estimated for healthcare professionals working in healthcare facilities identified by Cnam in September 2021 thanks to the RPPS (Shared Directory of Professionals involved in the Health System) and Adeli (Automation of Listings) and then paired with the COVID vaccine database. Among health professionals, only doctors, pharmacists, midwives, physiotherapists, dentists and nurses are identified in these directories. This estimation method was put in place as of 17 June 2021. As the identification of professionals through these directories dates back to September 2021, the estimates may include professionals who no longer practice in a healthcare facility and do not include professionals who have started their practice since that date. ### List of resources Liberal health professionals: — vacsi @-@ pss @-@ fra @-@ YYYY @-@ MM @-@ DD @-@ HHhmm.csv = > National level — vacsi @-@ pss @-@ a @-@ fra @-@ YYYY @-@ MM @-@ DD @-@ HHhmm.csv = > National level by age — vacsi @-@ pss @-@ s @-@ fra @-@ YYYY @-@ MM @-@ DD @-@ HHhmm.csv = > National level by sex — vacsi @-@ pss @-@ reg @-@ YYYY @-@ MM @-@ DD @-@ HHhmm.csv = > Regional level — vacsi @-@ pss @-@ dep @-@ YYYY @-@ MM @-@ DD @-@ HHhmm.csv = > Departmental Level Variables: — Territory of interest = dep or reg or fra — Date of injection = day — Vaccination coverage 1 dose = pss_couv_dose1 — Complete vaccination coverage = pss_couv_full — Vaccination coverage at least 1 booster dose = pss_couv_rappel — Vaccination coverage at least 2 booster dose = pss_couv_2_rappel — Vaccination coverage at least 3 booster dose = pss_couv_3_rappel — Vaccination coverage adapted to the Omicron variant of professionals = pss_couv_biv — Age group of interest = clage_vacsi — Sex of interest = sex ### Nomenclatures The age groups used are as follows: * 0: All ages * 09: 0-9 * 17: 10-17 * 24: 18-24 * 29: 25-29 * 39: 30-39 * 49: 40-49 * 59: 50-59 * 69: 60-69 * 74: 70-74 * 79: 75-79 * 80: 80 and + Sex is codified as follows: * 0: men + Women + Uninformed * 1: man * 2: woman The region (column ‘reg’) follows the codification of INSEE Official Geographical Code, it is codified as follows: * 01: Guadeloupe * 02: Martinique * 03: French Guiana * 04: Réunion * 11: Ile-de-France * 24: Centre-Val de Loire * 27: Burgundy-Franche-Comté * 28: Normandy * 32: Haut-de-France * 44: Great East * 52: Countries of the Loire * 53: Brittany * 75: New-Aquitaine * 76: Occitanie * 84: Auvergne-Rhône-Alpes * 93: Provence-Alpes-Côte d’Azur * 94: Corsica