En Francia, el número de enfermeras aumentó a 9,64 por cada 1000 personas en 2021 desde 9,43 por cada 1000 personas en 2020. Esta página incluye un gráfico con datos históricos para Enfermeras en Francia.

This dataset includes information published in Open Data by health insurance. It reports healthcare professionals practising in France. For each healthcare professional, the dataset provides the following information: - The profession of the health professional - Technical acts performed by the health professional. Matches for CCAM codes can be found here. - The agreement to which the professional is attached, and the average rates charged by the professional according to the medical procedure carried out - The use or non-use of teletransmission of care by the healthcare professional. - The address of the place where the trader practises and the associated geographical coordinates These data are updated every 3 months for Illness Insurance. This dataset is updated, compared to the data published by the health insurance on the 1st of each month.

Background: Nigeria’s healthcare system faces significant challenges in financing and quality, impacting the delivery of services to its growing population. This study investigates healthcare workers’ perceptions of these challenges and their implications for healthcare policy and practice. Methods: A cross-sectional survey was conducted with 600 healthcare professionals from eight states across Nigeria, representing a variety of healthcare occupations. Participants completed a questionnaire that assessed their perceptions of healthcare financing, quality of care, job satisfaction, and motivation using a 5-point Likert scale, closed- and open-ended questions. Descriptive statistics, Chi-squared test, and regression analysis were used to analyze the data. Results: The findings revealed that healthcare workers were generally not satisfied with the current state of healthcare financing and system quality in Nigeria. Poor funding, inadequate infrastructure, insufficient staffing, and limited access to essential resources were identified as major challenges. These challenges contributed to low job satisfaction, demotivation, and a desire to leave the profession. Socioeconomic factors, location State of practice, professional designation (clinical vs nonclinical), clinical designation (profession), and employment type (full-time vs part-time) were found to influence healthcare workers' perceptions (p < 0.05). Conclusion: The findings indicated a need to improve healthcare workers' satisfaction and retention, and quality of care in Nigeria, by increasing healthcare funding, transparent fund management protocols, investing in infrastructure and human resource development, and addressing regional healthcare disparities. By implementing these reforms, Nigeria can enhance the quality and accessibility of healthcare services and improve the health and well-being of its citizens.

Access Healthcare Professionals data for European healthcare and hospital executives with Success.ai. Includes contact details, professional insights, and decision-maker profiles from 70M+ businesses. GDPR-compliant. Best price guaranteed.

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).

The COVID-19 pandemic was making a huge impact on Europe’s healthcare systems in the spring of 2020, and most predictive models concurred that pandemic waves were in the offing. Most studies adopted a pathogenic approach to the subject; few used a salutogenic approach. These showed, however, that nurses can retain their health despite a pandemic by mobilising generalised resistance resources. Our study aims to understand how nurses working in hospitals protected their health and workplace well-being during the COVID-19 pandemic by investigating the moderating effects of the health resources they mobilised against the stressors inherent to the situation. Data was gathered longitudinally in the following countries: Switzerland (French-speaking and German-speaking parts), France, Portugal and Canada. In addition, a cross-sectionnal sample of nurses from Belgium was also investigated. The questionnaires included the PSS, WHOQOL, NSS, BRIEF-COPE, PTGI, CD-RISC, MSPSS, COPSOQ, SISI and demographic information. See Ortololeva et al. 2021 (in the bibliographical reference section) for the published protocol of this project

BackgroundInfluenza-vaccination rates among healthcare workers (HCW) remain low worldwide, even during the 2009 A(H1N1) pandemic. In France, this vaccination is free but administered on a voluntary basis. We investigated the factors influencing HCW influenza vaccination. MethodsIn June–July 2010, HCW from wards of five French hospitals completed a cross-sectional survey. A multifaceted campaign aimed at improving vaccination coverage in this hospital group was conducted before and during the 2009 pandemic. Using an anonymous self-administered questionnaire, we assessed the relationships between seasonal (SIV) and pandemic (PIV) influenza vaccinations, and sociodemographic and professional characteristics, previous and current vaccination statuses, and 33 statements investigating 10 sociocognitive domains. The sociocognitive domains describing HCWs' SIV and PIV profiles were analyzed using the classification-and-regression–tree method. ResultsOf the HCWs responding to our survey, 1480 were paramedical and 401 were medical with 2009 vaccination rates of 30% and 58% for SIV and 21% and 71% for PIV, respectively (p

Provides information on the total, primary, equal and secondary use of French at work for workers employed in selected healthcare occupations, please see footnotes for more details. These data are based on the 2001, 2006 and 2016 Censuses of Population as well as the 2011 National Household Survey.

Provides information on the total, primary, equal and secondary use of French at work for workers employed in selected healthcare occupations, please see footnotes for more details. These data are based on the 2001, 2006 and 2016 Censuses of Population as well as the 2011 National Household Survey.

France FR: Nurses and Midwives: per 1000 People data was reported at 10.605 Ratio in 2015. This records an increase from the previous number of 7.943 Ratio for 2008. France FR: Nurses and Midwives: per 1000 People data is updated yearly, averaging 7.262 Ratio from Dec 1991 (Median) to 2015, with 13 observations. The data reached an all-time high of 10.605 Ratio in 2015 and a record low of 5.575 Ratio in 1991. France FR: Nurses and Midwives: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank.WDI: Health Statistics. Nurses and midwives include professional nurses, professional midwives, auxiliary nurses, auxiliary midwives, enrolled nurses, enrolled midwives and other associated personnel, such as dental nurses and primary care nurses.; ; World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data.; Weighted average;

IntroductionForty years passed between the two most important definitions of primary health care from Alma Alta Conference in 1978 to WHO’s definition in 2018. Since then, reforms of healthcare systems, changes in ambulatory sector and COVID 19, have created a need for reinterpretations and redefinition of primary healthcare. The primary objective of the study was to precise the definitions and the representations of primary healthcare by healthcare professionals.MethodsWe conducted a descriptive cross-sectional study using a web-based anonymized questionnaire including opened-ended and closed-ended questions but also “real-life” case-vignettes to assess participant’s perception of primary healthcare, from September to December 2020. Five case-vignette, describing situations involving a specific primary health care professional in a particular place for a determined task were selected, before the study, by test/retest method.ResultsA total of 585 healthcare practitioners were included in the study, 29% were general practitioners and 32% were midwives. Amongst proposed healthcare professions, general practitioners (97.6%), nurses (85.3%), midwives (85.2%) and pharmacists (79.3%) were those most associated with primary healthcare. The functions most associated with primary healthcare, with over 90% of approval were “prevention, screening”, “education to good health”, “orientation in health system”. Two case-vignettes strongly emerged as describing a situation of primary healthcare: Midwife/Hospital/Pregnancy (74%) and Pharmacist/Pharmacy/Flu shot (90%). The profession and the modality of practice of the responders lead to diverging answers regarding their primary healthcare representations.ConclusionsPrimary healthcare is an ever-evolving part of the healthcare system, as is its definition. This study explored the perception of primary healthcare by French healthcare practitioners in two complementary ways: oriented way for the important functions and more practical way with the case-vignettes. Understanding their differences of representation, according to their profession and practice offered the authors a first step to a shared and operational version of the primary healthcare definition.

Ebitda Time Series for M3 Inc. M3, Inc. provides medical-related services primarily to physicians and other healthcare professionals through the Internet. The company operates through five reporting segments: Medical Platform, Evidence Solutions, Career Solutions, Site Solutions, and Overseas. It operates m3.com, a members-only website for providing information to healthcare professionals; MR-kun, where member doctors can independently and continuously receive information on the m3com platform; AskDoctors, where registered doctors answer questions about health and illness from the public; MDLinx for medical professionals in the United States; and Doctors.net.uk, a website that provides developing services for pharmaceutical companies, as well as provides drug information database in France, Germany, and Spain. The company also provides career services for doctors and pharmacists, recruitment, referrals and posting job advertisements through n3.com CAREER. In addition, it engages in the sales activities and marketing operations for pharmaceuticals and medical devices; development, sale, and support business of electronic medical records and medical equipment for medical institutions; survey service for medical professionals; sale and marketing support businesses for pharmaceutical companies, etc. through the Internet; provision of management support and consulting services to medical institutions, and home-visit nursing services; and provision of human resources services for healthcare professionals, as well as operates clinical trial facilities. Further, M3, Inc. offers clinical and medico-political news and education for medical professionals. The company was formerly known as So-netM3, Inc. and changed its name to M3, Inc. in January 2010. M3, Inc. was incorporated in 2000 and is headquartered in Tokyo, Japan.

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 information on the patientele of liberal healthcare professionals: * number of unique patients (active file); * number of “doctor treating” patients (only for general physicians and pediatricians). Several territorial levels are available: national level (whole France), region, department. 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. ### Statistical confidentiality: Out of respect for statistical confidentiality (Law of 7 June 1951) and in order that direct or indirect identification of individuals is impossible, no information on fees, prescriptions and patient care is provided when the number of liberal health professionals is strictly less than 5. The value of the indicator is then indicated by “NS” (not significant) in the dataset. ### Abbreviations present in the data: * “NS” = non-significant (application of statistical confidentiality) * “NC” = not calculated (occupation not concerned, etc.) ### Data update: The data proposed for download in the “Export” tab is updated every year (data from the whole of France since 2016).

The health directory responds to the general mission of informing insured persons with a view in particular to facilitating access to care, entrusted to the Health Insurance in accordance with Article L.162-1-11 of the Code of Social Security. It makes it possible to find the contact details of health professionals practising in a liberal capacity and those of healthcare facilities, as well as the acts performed. It also makes it possible to know the conventional sector to which a healthcare professional belongs, the rates charged, whether or not he accepts the vital card, or the pricing data for certain hospitalisation services. This dataset contains personal information about health professionals. It is published in accordance with the provisions of Article L. 1461-2 of the Public Health Code. The re-use of this data is subject to compliance with privacy regulations. This dataset is updated monthly.

Dataset for MedAID-ML: A Multilingual Dataset of Biomedical Texts for Detecting AI-Generated Content

This dataset was created by gathering human-authored corpora from several public health sites and generating additional data via three different LLMs: GPT-4o, Mistral-7B and Llama3-1. We included texts in English, Spanish, German and French data from the biomedical domain. The current version gathers 50% AI-generated and 50% human-written texts.

The following are the data we used:

• Cochrane Library: This is a database of meta-analyses and systematic reviews of updated results of clinical studies. We used abstracts of systematic reviews in all four languages.
• European Clinical Trials (EUCT): This agency that supervises and evaluates pharmaceutical products of the European Union (EU). We downloaded parallel data from public assessment reports (EPARs) from 12 new medicinal products, and data from clinical trial protocols and eligibility criteria. We ensured the data were published only from January 2025 to date. The goal was gathering data that might not have been used to train the LLMs in our experiments.
• European Medicines Agency (EMA): This agency that supervises and evaluates pharmaceutical products of the European Union (EU). We downloaded parallel data from public assessment reports (EPARs) from 12 new medicinal products, and data from clinical trial protocols and eligibility criteria. We ensured the data were published only from January 2025 to date. The goal was gathering data that might not have been used to train the LLMs in our experiments.
• European Food Safety Authority (EFSA): This website provides a comprehensive range of data about food consumption and chemical/biological monitoring data. We chose only the topics we deem necessary for our goals, therefore including a total of 51 topics. Processing: we manually split articles with a wordcount of above 1350 and manually ensured their correctness and alignment in all languages.
• European Vaccination Information Portal (EVIP): it provides up-to-date information on vaccines and vaccination. The factsheets are available in all languages, and consist of 20 texts each.
• Immunize: Immunize.org (formerly known as the Immunization Action Coalition) is a U.S.-based organization dedicated to providing comprehensive immunization resources for healthcare professionals and the public. Vaccine Information Sheets (VISs) have been translated into several languages, but not all of them contain all VISs. They are given as PDFs, with 25 in Spanish, French and English, but only 21 in German. Only PDFs overlapping in all languages were used.
• Migration und Gesundheit - German Ministry of Health (BFG): This portal provides multilingual health information tailored for migrants and refugees. Gesundheit für alle is a PDF file that provides a guide to the German healthcare system, and it is available in Spanish, English and German. Processing: Two topics, which were shorter than 100 words, were merged with the next one to ensure that context is preserved.
• Orphadata (INSERM): a comprehensive knowledge base about rare diseases and orphan drugs, in re-usable and high-quality formats, released in 12 official EU languages. We gathered definitions, signs and symptoms and phenotypes about 4389 rare diseases in English, German, Spanish and French. Processing: Since each definition is roughly the same size and similar format, we simply group 5 definitions together to make the text per topic longer.
• PubMed (National Library of Medicine): we downloaded abstracts available in English, Spanish, French and German.
• Wikipedia: a free, web-based, collaborative multilingual encyclopedia project; we selected (bio)medical contents available in English, German, Spanish and French. To ensure that the texts were not automatically generated, we only use articles that date back to before the release of ChatGPT, i.e. before 30th November 2022. Processing: some data cleaning was necessary; we also removed all topics with less than 5 words, or split those with more than 9 sentences into equally long parts. From these split up files, we make sure that they contain a minimum of 100 words, and we take only those contents or topics that exist in all three languages.

Description of methods used for collection/generation of data

The corpus statistics and methods are explained in the following article: Patrick Styll, Leonardo Campillos-Llanos, Jorge Fernández-García, Isabel Segura-Bedmar (2025) "MedAID-ML: A Multilingual Dataset of Biomedical Texts for Detecting AI-Generated Content".

Methods for processing the data

• Web-scraping of data from HTML content and PDF files available on the websites of the health contents.
• Postprocessing and cleaning of data (e.g., removal of redundant white spaces or line breaks), and homogeneization of text length.
• Generation of corresponding contents by means of generative AI using three large language models: GPT-4o, Mistral-7B and Llama3-1. - Formating of contents into JSON format.

Files

JSON files:.These are separated in TRAIN and TEST. Each file has a list of hashes for each text, and each hash contains the following fields:

• text: the textual content.
• data_source: the source repository of the text.
• filename: the name of the original file from which the data were sourced.
• source: label indicating if it is a human-written text (HUMAN) or the LLM used to generate the text ("gpt4o", "mistral" or "llama").
• language: The language code of the text: German ("de"), English ("en"), Spanish ("es") or French ("fr").
• target: a binary label to code if the text is written by humans ("0") or AI ("1").
• ratio: The proportion of the text that was created with AI: "0.5" for AI-generated texts, and "null" for human texts.

Success.ai’s European Healthcare Company Dataset provides verified contact data for professionals across the region’s medical and pharmaceutical industries. Whether targeting private hospitals, biotech startups, or pharmaceutical giants, this dataset gives you compliant, high-accuracy access to decision-makers and practitioners.
Designed for medtech vendors, CROs, pharma suppliers, and staffing agencies, this dataset includes full contact details, firmographics, and regional segmentation for over 3 million verified professionals.

What You Get:
- Work email and phone (where available)
- Job title, specialty, and seniority
- Company name, sector, and size
- LinkedIn URLs and region

Use Cases:
- Medtech B2B sales
- Pharmaceutical marketing
- Research recruitment & outreach
- Healthcare SaaS sales in Europe

Data on use of English and French at work by healthcare professionals living in private households, by occupation and first official language spoken.

This dataset is derived from the data processing of the health directory published by the National Health Insurance Fund (https://data.opendatasoft.com/explore/dataset/mede...): Each registration corresponds to a healthcare professional practising in France and details: - The profession of the health professional - Its location and coordinates The nature of its activity and the agreement under which it carries out - The technical acts he performs This dataset is updated monthly.

IntroductionTo develop high-quality and safe healthcare, a good safety culture is an important feature of healthcare-providing structures. The objective of this study was to analyze the qualitative data of the comments section of a Hospital Survey on Patient Safety (HSOPS) questionnaire to clarify the answers given to the closed questions.MethodUsing the original data from a cross-sectional survey of 5,064 employees at a single university hospital in France, we conducted a qualitative study by analyzing the comments of a HSOPS survey and conducting in-depth interviews with 19 healthcare providers. We submitted the comments and the interviews to a thematic analysis.ResultsA total of 3,978 questionnaires were returned, with 247 comments collected. The qualitative analysis identified several structural failures. The main categories of the open comments were concordant with the lowest dimension scores found in the quantitative analysis. The most frequently reported failures were related to the staffing and hospital management support dimensions. The healthcare professionals perceived the lack of resources, including understaffing, as the major barrier to the development of a patient safety culture. Concrete organizational issues related to hospital handoffs and risk coordination were identified, such as transfers from the emergency departments and the lack of feedback following self-reporting of incidents.ConclusionThe analysis of the open comments complemented the HSOPS scores, increasing the level of detail in the description of the hospital’s patient safety culture. Combined with a classical quantitative approach used in HSOPS-based surveys, the qualitative analysis of open comments is useful to identify organizational weaknesses within the hospital.

Multicultural Amazonian populations in remote areas of French Guiana face challenges in accessing healthcare and preventive measures. They are geographically and administratively isolated. Health mediation serves as an interface between vulnerable people and the professionals involved in their care. This approach aims to improve the health of Amazonian populations by addressing their unique challenges, including social and health vulnerabilities, as well as language and cultural barriers. A Mobile Public Health Team (MPHT) relying on health mediation was created in 2019. Comprising six nurse–community-health mediator pairs who receive ongoing specialised training, along with a coordination team of one physician and two public health nurses, the MPHT is connected to the 17 Prevention and Care Remote Centres across the territory. This article presents a community case study of the MPHT of the remote areas in French Guiana and the description of the activities of this health promotion programme in the context of the COVID-19 pandemic in 2021. The MPHT carried out health promotion initiatives, often in collaboration with partners, focusing on health priorities of the Amazonian territories. The interventions were co-designed with community leaders and local populations to ensure relevance and effectiveness. In response to the COVID-19 pandemic, the MPHT reached over 6,000 individuals in addition to more than 3,000 participants in a water, hygiene and sanitation education programme. The team performed 83 health promotion interventions on eight different topics, including 28 in the general population (922 people reached) and 55 in schools (n = 930). The MPHT produced 20 communication tools, which were adapted and translated into eight languages. The team also participated in managing six simultaneous epidemic events, including malaria, diphtheria, and tuberculosis. This study highlights how the combined expertise of healthcare professionals and the mediation skills of community health workers effectively addressed the specific health needs of the multicultural Amazonian populations. This model for addressing social and health inequities should encourage institutional recognition of the community health mediator model.