This dataset is one of the sources of data visualisations available on the [Liberal Health Professionals] website(https://data.ameli.fr/pages/data-professionnels-sante-liberaux/). ### General information: The liberal health professions available in this dataset are: * the doctors (with more than twenty medical specialties); * dental surgeons** (including dentofacial orthopaedic specialists – ODF); * the women; * medical assistants with five professions: nurses, massage therapists, speech therapists, orthoptists, pedicures-podologists. They are health professionals active on 31 December of the year concerned and: * exercising their activity as a liberal; * in metropolitan France, Guadeloupe, French Guiana, Reunion, Martinique and Mayotte; * having received at least EUR 1 in fees; * whether they are contracted with the Sickness Insurance or not (when they generate a prescription reimbursed by the Sickness Insurance); * professionals in employment-retirement cumulation are counted in the workforce as long as they meet the previous conditions. This dataset presents demographic information about liberal healthcare professionals such as: *average ages: * women; * men; * global; * share of women; * share of men; * share 60 years of age and older; * share of under 60s. This dataset is complementary to the following dataset: Liberal health professionals: number and density by age group, sex and territory (department, region). Only the national level is available for this data. The data are derived from the National Health Data System (NSDS). For more information (source, field, definitions of modalities), visit the Method page of this site. ### Data update: The data proposed for download in the “Export” tab is updated every year (data from the whole of France since 2010).

This table contains 6720 series, with data for years 1994 - 1998 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (5 items: Territories; Yukon; Northwest Territories including Nunavut; Northwest Territories ...), Age group (14 items: Total; 12 years and over; 12-19 years; 12-14 years; 15-19 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; High 95% confidence interval - number of persons; Coefficient of variation for number of persons; Low 95% confidence interval - number of persons ...).

This table contains 205632 series, with data for years 2005 - 2005 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (160 items: Canada; Central Regional Integrated Health Authority; Newfoundland and Labrador; Newfoundland and Labrador; Eastern Regional Integrated Health Authority; Newfoundland and Labrador ...) Age group (14 items: Total; 12 years and over; 12 to 19 years; 12 to 14 years; 15 to 19 years ...) Sex (3 items: Both sexes; Males; Females ...) Contact with medical doctors (4 items: Total population for the variable contact with medical doctors; Contact with medical doctors in the past 12 months; No contact with medical doctors in the past 12 months; Contact with medical doctors in the past 12 months; not stated ...) Characteristics (8 items: Number of persons; High 95% confidence interval; number of persons; Coefficient of variation for number of persons; Low 95% confidence interval; number of persons ...).

Medical doctors (%)

  Dataset Description

This dataset provides information on 'Medical doctors' for countries in the WHO African Region. The data is disaggregated by the 'Sex' dimension, allowing for analysis of health inequalities across different population subgroups. Units: %

  Dimensions and Subgroups

Dimension: Sex Available Subgroups: Female, Male

  Data Structure

The dataset is in a wide format.

Index: Year (formatted as YYYY-01-01) Columns:… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/medical-doctorsby-sex-for-african-countries.

*WHO-5, World Health Organization-Five Well-being Index.†Fisher's exact test or trend test between any medical error and no error.

This table contains 14784 series, with data for years 1994 - 1998 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (11 items: Canada; Nova Scotia; Newfoundland and Labrador; Prince Edward Island ...), Age group (14 items: Total; 12 years and over; 12-19 years; 12-14 years; 15-19 years ...), Sex (3 items: Both sexes; Males; Females ...), Contact with medical doctors (4 items: Total population for the variable contact with medical doctors; Contact with medical doctors; not stated; No contact with medical doctors in past 12 months; Contact with medical doctors in past 12 months ...), Characteristics (8 items: Number of persons; Coefficient of variation for number of persons; Low 95% confidence interval - number of persons; High 95% confidence interval - number of persons ...).

This table contains 127008 series, with data for years 2005 - 2005 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (126 items: Canada; Newfoundland and Labrador; Central Regional Integrated Health Authority; Newfoundland and Labrador; Eastern Regional Integrated Health Authority; Newfoundland and Labrador ...) Age group (6 items: Total; 12 years and over; 12 to 19 years ...) Sex (3 items: Both sexes; Males; Females ...) Regular medical doctor (7 items: Total population for the variable regular medical doctor; Has not looked for a regular medical doctor; Cannot find a regular medical doctor; Has a regular medical doctor ...) Characteristics (8 items: Number of persons; Coefficient of variation for number of persons; High 95% confidence interval; number of persons; Low 95% confidence interval; number of persons ...).

Please provide the following data regarding the prescribing of CNS stimulants and ADHD medicines (BNF 68 section 4.4) in England: • Number of patients prescribed CNS stimulants and ADHD medicines between January 2015 and January 2023 broken down by: - Month; - Age group (0-17 years, 18+ years); - Gender (Male and Female). - Chemical substance Response A copy of the information is attached. NHS Prescription Services process prescriptions for Pharmacy Contractors, Appliance Contractors, Dispensing Doctors and Personal Administration with information then used to make payments to pharmacists and appliance contractors in England for prescriptions dispensed in primary care settings (other arrangements are in place for making payments to Dispensing Doctors and Personal Administration). This involves processing over 1 billion prescription items and payments totalling over £9 billion each year. The information gathered from this process is then used to provide information on costs and trends in prescribing in England and Wales to over 25,000 registered NHS and Department of Health and Social Care users. Data Source The data source was the NHSBSA Information Services Data Warehouse. Exclusions The Data excludes: • Items not dispensed, disallowed and those returned to the contractor for further clarification. • Prescriptions prescribed and dispensed in Prisons, Hospitals and Private prescriptions. • Items prescribed but not presented for dispensing or not submitted to NHS Prescription Services by the dispenser. Time Period April 2015 to January 2023 inclusive. Patient Data is available from April 2015 onwards. Organisation Data Only items that were prescribed in England and dispensed in the community have been included. Year Month The year and month for which the claim for dispensed items has been submitted to NHSBSA. BNF Chemical Substance The nine characters at the beginning of a BNF code which specify the Chemical substance of a drug. Gender_PDS Whether an identified patient is (male or female) has been determined using the latest patient gender information held by the NHSBSA Information Services data warehouse at the time that the prescription data was loaded. Patient gender information is updated periodically - sometime after the data has been loaded - using information from NHS Personal Demographics Service (PDS). At the time that prescription data is loaded the PDS data held by NHSBSA may be incomplete or may not reflect the latest data held by PDS. Patient gender cannot be reported for prescriptions for which the NHS number could not be captured or where no corresponding PDS data is held by NHSBSA. Prescriptions used in this dataset have been limited to where the data held in the NHSBSA data Warehouse has been recorded as male or female. The following percentage of prescription items within the dataset had a recorded gender of male or female, by calendar year: 2015 (April to December) 79.36% 2016 81.66% 2017 81.69% 2018 81.72% 2019 83.97% 2020 87.83% 2021 87.65% 2022 87.56% 2023 (January) 87.56% Age Patient age is as captured on prescriptions during processing. Patients may appear in more than one age group if they have prescribing in more than one age group therefore patient counts should not be added together, and they should only be used as presented in this request. Data has been limited to prescriptions where an age has been captured, the following percentage of prescription items within the dataset had a recorded age, by calendar year;

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

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

BackgroundHaoxinqing, China’s largest online mental health platform, facilitates digital psychological care delivery. This study aims to describe the demographics and medical service data of doctors on the Haoxinqing platform and investigate their associations.MethodThe study analyzed the demographic information and medical service data of 11,333 registered physician users on the Haoxinqing platform over a 5-year period.ResultAmong registered physicians, 87.0% were from secondary or tertiary hospitals and were concentrated in eastern provinces (e.g., Guangdong: 918). Female physicians had a lower proportion in senior titles (chief physicians: 19.0% vs. 20.0% for males), although the chi-square analysis indicated a weak association between gender and professional title (Cramer’s V = 0.051, P < 0.001). Text and image consultations dominate (82.1%). Professional titles significantly impacted service volume: chief physicians had 3.85 times more patients (IRR = 3.85, 95% CI [2.11–7.00]) and prescribed 4.16 times more medications (IRR = 4.16, 95% CI [3.21–5.41]) than residents (P < 0.001). Negative binomial regression showed that male physicians had 30% fewer patients than females (IRR = 0.70, 95% CI [0.58–0.85], P < 0.001), but the effect size for the association between gender and consultation methods was low (Cramer’s V = 0.036).ConclusionBased on cross-sectional data from China’s largest online mental health platform, this study revealed that online services, while supplementing offline medical care, are still influenced by traditional medical hierarchy. Patients’ trust in senior physicians and gendered communication norms are critical determinants affecting resource allocation patterns on digital platforms.

This is a subset of the data from https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0146837 Pan X, Slater M, Beacco A, Navarro X, Bellido Rivas AI, Swapp D, et al. (2016) The Responses of Medical General Practitioners to Unreasonable Patient Demand for Antibiotics - A Study of Medical Ethics Using Immersive Virtual Reality. PLoS ONE 11(2): e0146837. https://doi.org/10.1371/journal.pone.0146837

There are 21 doctors in a virtual reality experiment, confronted by a patient who is an older woman and her daughter. The patient has a cough. The daughter insists on an antibiotic prescription even though the indications are a viral infection. The doctors were either established general practitioners (GPs) or trainees. The point of interest is whether the GPs were more likely to resist the antibiotic prescription.

gp = 1 general practitioner, 0 traineed. gender = 2 female, 1 male agreeableness and conscientiousness are from the "big five" personality inventory. prescribe = 1 if they prescribed antibiotics and 0 otherwise. behave is answer to the question "How much did you behave within the consultation room as if the situation were real? (Not at all 1…7 very much)." reason records the reason for their action: 1: Common situation 2: Pressured 3: Could give a delayed prescription 4: Medical reasons

Associations between the gender of physicians and the perspective of patients on their opportunity to engage with SDM.

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 table contains 12960 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 (15 items: Canada; Nova Scotia; Prince Edward Island; Newfoundland and Labrador ...) Age group (12 items: Total; 15 years and over; 20-24 years; 20-34 years; 15-19 years ...) Sex (3 items: Both sexes; Females; Males ...) Patient satisfaction - family doctor or other physician care (3 items: Received family doctor or other physician care in past 12 months; Very or somewhat satisfied with family doctor or other physician care received; Quality of family doctor or other physician care received rated as excellent or good ...) Characteristics (8 items: Number of persons; Low 95% confidence interval - number of persons; Coefficient of variation for number of persons; High 95% confidence interval - number of persons ...).

This table contains 205632 series, with data for years 2005 - 2005 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (160 items: Canada; Central Regional Integrated Health Authority; Newfoundland and Labrador; Newfoundland and Labrador; Eastern Regional Integrated Health Authority; Newfoundland and Labrador ...) Age group (14 items: Total; 12 years and over; 12 to 19 years; 12 to 14 years; 15 to 19 years ...) Sex (3 items: Both sexes; Males; Females ...) Contact with medical doctors (4 items: Total population for the variable contact with medical doctors; Contact with medical doctors in the past 12 months; No contact with medical doctors in the past 12 months; Contact with medical doctors in the past 12 months; not stated ...) Characteristics (8 items: Number of persons; High 95% confidence interval; number of persons; Coefficient of variation for number of persons; Low 95% confidence interval; number of persons ...).

A collection of data of an epidemiological study of chronic disease in the oldest old based on information collected from Kaiser Permanente facilities in Northern California (KPNC). The initial sample was drawn from the Kaiser''s active membership lists for the years 1971 and 1980. The sample was restricted to members that had a Multiphasic Health Checkup examination (MHC) within 7 years of the baseline date. The sample was stratified to attain equal numbers of observations (1,000 in each) in three sex-age cells for each cohort: 65-69, 70-79, and 80+. Each cohort was followed for 9 years through existing medical records and computerized hospitalization tapes. Mortality data was collected by matching the sampled data with state Vital Statistics data for an additional 3 years for a total follow-up time of 12 years. Part 1 of the data collections consists of Master Records, which includes information from the morbidity review, in which over 35 chronic conditions or diagnoses were abstracted from the member charts, as well as detailed diagnostic criteria for the major conditions. A prevalence review was done, which included the 4 years prior to the baseline date for these same conditions. Recurrent disease is included for the following conditions: cancers, myocardial infarction, and various forms of strokes. A detailed account of outpatient health services use, and data from the multiphasic health checkup, which was administered to each participant during the nine yearly follow-ups, are also included in the Master Records file. The labs and procedures included: chemistry, hematology, urinalysis, bacteriology, chest x-ray, GI x-ray, ultrasound, CT/MRI, mammogram, resting ECG, treadmill ECG, echocardiograms, nuclear scans, outpatient breast biopsy, cystoscopy, and cataract surgery. Inpatient utilization includes all hospitalizations, procedures done during a hospital stay, length of stay, admitting/discharge diagnosis. Part 2, Hospitalization, contains records of causes and dates of hospitalizations and discharges and nursing home admissions. There is also a section on incomplete reviews and the reasons for them. Demographic information and some lifestyle information from the multiphasic health checkup (e.g., smoking, alcohol, and Body Mass Index) are also in this file. Data Availability: These datasets have been documented extensively and are available from the ICPSR (Study No. 4219). * Dates of Study: 1971-1992 * Study Features: Longitudinal, Anthropometric Measures * Sample Size: ** 1971 cohort: 2,877 (baseline) ** 1980 cohort: 3,113 (baseline) ** 1971 & 1980: 5,990 ** Hospitalization: 14,730 Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/04219 * HSRR: http://wwwcf.nlm.nih.gov/hsrr_search/view_hsrr_record_table.cfm?TITLE_ID=381&PROGRAM_CAME=toc_with_source2.cfm

The anemia dataset used in this study were obtained from the Faculty of Medicine, Tokat Gaziosmanpaşa University, Turkey. The data contains the complete blood count test results of 15,300 patients in the 5-year interval between 2013 and 2018. The dataset of pregnant women, children, and patients with cancer were excluded from the study. The noise in the dataset was eliminated and the parameters, which were considered insignificant in the diagnosis of anemia, were excluded from the dataset with the help of the experts. It is observed that, in the dataset, some of the records have missing parameter values and have values outside the reference range of the parameters which are marked by specialist doctors as noise in our study. Thus, records that have missing data and parameter values outside the reference ranges were removed from the dataset. In the study, Pearson correlation method was used to understand whether there is any relationship between the parameters. It is observed that the relationship between the parameters in the dataset is generally a weak relationship which is below p < 0.4 [59]. Because of this reason none of the parameters excluded from the dataset. Twenty-four features (Table 1) and 5 classes in the dataset were used in the study (Table 2). Since the difference between the parameters in the dataset was very high, a linear transformation was performed on the data with min-max normalization [30]. This dataset consists of data from 15,300 patients, of which 10,379 were female and 4921 were male. The dataset consists of 1019 (7%) patients with HGB-anemia, 4182 (27%) patients with iron deficiency, 199 (1%) patients with B12 deficiency, 153 (1%) patients with folate deficiency, and 9747 (64%) patients who had no anemia (Table 2). The transferring saturation in the dataset was obtained by the "SDTSD" feature, using the Eq. (1), which was developed with the help of a specialist physician. Saturation is the ratio of serum iron to total serum iron. In the Equation SD represents Serum Iron and TSD represents Total Serum Iron.

These datasets were collected to fulfil the requirement of University coursework.

The complete source code and paper are available on GitHub. Click here.

About Dataset

These datasets contain the information of the World Development Indicator (WDI) provided by the world bank, the non-communicable mortality rate, the suicide rate and the number of health workforce data by the World Health Organization (WHO).

Implementation

• Data Cleaning and Preparation
• Exploratory Data Analysis
• Machine Learning Prediction - Random Forest
• Deep Learning Prediction - Neural Network

This table contains 12960 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 (15 items: Canada; Nova Scotia; Prince Edward Island; Newfoundland and Labrador ...) Age group (12 items: Total; 15 years and over; 20-24 years; 20-34 years; 15-19 years ...) Sex (3 items: Both sexes; Females; Males ...) Patient satisfaction - family doctor or other physician care (3 items: Received family doctor or other physician care in past 12 months; Very or somewhat satisfied with family doctor or other physician care received; Quality of family doctor or other physician care received rated as excellent or good ...) Characteristics (8 items: Number of persons; Low 95% confidence interval - number of persons; Coefficient of variation for number of persons; High 95% confidence interval - number of persons ...).

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