This data set contains the number of students appearing for Foreign Medical Graduate Examination (FMGE)

Note: The data for 2012 to 2014 and 2015 to 2018 are mentioned cumulatively, no year wise categorisation for these years is available.

The dataset contains answers from a questionnaire distributed to all medical students at UiT as well as first year graduates from November 2019 to February 2020. The purpose of the questionnaire was to investigate how the UiT Medical students acquire practical competence in emergency medicine-related skills, and to investigate whether students with extracurricular healthcare-related work experience had more training and confidence in such skills than students without such experience. Data such as ECHR work experience (yes, no) and workplace, work length (<6 months, 6 months-1 year, 1-3 years, >3 years), work hours (<10h, 10-100h, 101-200h, 201-300h, 301-500h, >500h) and number of workplaces (1, >1), as well as year of study (years 1-6 and first year graduates), previous healthcare-related education (no, commenced but unfinished, finished), previous military medic-training (no, basic, advanced), and number of TAMS events participated in (0, 1, 2-5, 6-10, >10) were recorded as well, and included in the data analysis as predictors and confounders. Several items probing amount of training as well as confidence level for the respective procedures were created as well, as Likert-based items. The alternatives for training amount were 0, 1-5, 6-10, 11-30, >30 times for most items, however, for some, training amount in practice (0, 1-5, 6-10, 11-30, >30 times) and real-life situations (0, 1, 2-5, 6-10, >10) were probed separately. Confidence level was probed as degree of agreement, from strongly disagree to strongly agree. At the bottom of the dataset, variables from calculations of the data are included, such as median, mean and sum of the variables addressing training amount and confidence level, respectively. These composite scores were applied for statistical analyses. Abstract Objectives: To study the association between medical students' extracurricular healthcare-related (ECHR) work experience and their self-reported practical experience and confidence in selected emergency medicine procedures. Study design: Cross-sectional study. Materials and methods: Medical students and first-year graduates were invited to answer a Likert-based questionnaire probing self-reported practical experience and confidence with selected emergency medicine procedures. Participants also reported ECHR work experience, year of study, previous healthcare-related education, military medic-training and participation in the local student association for emergency medicine (TAMS). Differences within the variables were analyzed with independent samples t-tests, and correlation between training and confidence was calculated. Analysis of covariance and mixed models were applied to study associations between training and confidence, and work experience (primary outcomes) and the other reported factors (secondary outcomes) respectively. Cohen’s D was applied to better illustrate the strength of association for primary outcomes. Results: 539 participants responded (70%). Among these, 81% had ECHR work experience. There was a strong correlation (r=0.878) between training and confidence. Work experience accounted for 5.9% and 3.5% of the total variance in training and confidence (primary outcomes), and respondents with work experience scored significantly higher than respondents without work experience. Year of study, previous education, military medic-training and TAMS-participation accounted for 49.3% and 58.5%, 8.7% and 5.1%, 6.8% and 4.7%, and 23.6% and 12.3% of the total variance in training and confidence respectively (secondary outcomes). Cohen’s D was 0.48 for training amount and 0.32 for confidence level, suggesting medium and weak-medium sized associations to work experience, respectively. Conclusions: ECHR work experience is common among medical students, and was associated with more training and higher confidence in the investigated procedures. Significant associations were also seen between training and confidence, and year of study, previous healthcare-related education and TAMS participation, but military medic-training showed no association.

The dataset contains answers from a questionnaire distributed to all medical students at UiT as well as first year graduates from November 2019 to February 2020. The purpose of the questionnaire was to investigate how the UiT Medical students acquire practical competence in emergency medicine-related skills, and to investigate whether students with extracurricular healthcare-related work experience had more training and confidence in such skills than students without such experience. Data such as ECHR work experience (yes, no) and workplace, work length (<6 months, 6 months-1 year, 1-3 years, >3 years), work hours (<10h, 10-100h, 101-200h, 201-300h, 301-500h, >500h) and number of workplaces (1, >1), as well as year of study (years 1-6 and first year graduates), previous healthcare-related education (no, commenced but unfinished, finished), previous military medic-training (no, basic, advanced), and number of TAMS events participated in (0, 1, 2-5, 6-10, >10) were recorded as well, and included in the data analysis as predictors and confounders. Several items probing amount of training as well as confidence level for the respective procedures were created as well, as Likert-based items. The alternatives for training amount were 0, 1-5, 6-10, 11-30, >30 times for most items, however, for some, training amount in practice (0, 1-5, 6-10, 11-30, >30 times) and real-life situations (0, 1, 2-5, 6-10, >10) were probed separately. Confidence level was probed as degree of agreement, from strongly disagree to strongly agree. At the bottom of the dataset, variables from calculations of the data are included, such as median, mean and sum of the variables addressing training amount and confidence level, respectively. These composite scores were applied for statistical analyses. Abstract Objectives: To study the association between medical students' extracurricular healthcare-related (ECHR) work experience and their self-reported practical experience and confidence in selected emergency medicine procedures. Study design: Cross-sectional study. Materials and methods: Medical students and first-year graduates were invited to answer a Likert-based questionnaire probing self-reported practical experience and confidence with selected emergency medicine procedures. Participants also reported ECHR work experience, year of study, previous healthcare-related education, military medic-training and participation in the local student association for emergency medicine (TAMS). Differences within the variables were analyzed with independent samples t-tests, and correlation between training and confidence was calculated. Analysis of covariance and mixed models were applied to study associations between training and confidence, and work experience (primary outcomes) and the other reported factors (secondary outcomes) respectively. Cohen’s D was applied to better illustrate the strength of association for primary outcomes. Results: 539 participants responded (70%). Among these, 81% had ECHR work experience. There was a strong correlation (r=0.878) between training and confidence. Work experience accounted for 5.9% and 3.5% of the total variance in training and confidence (primary outcomes), and respondents with work experience scored significantly higher than respondents without work experience. Year of study, previous education, military medic-training and TAMS-participation accounted for 49.3% and 58.5%, 8.7% and 5.1%, 6.8% and 4.7%, and 23.6% and 12.3% of the total variance in training and confidence respectively (secondary outcomes). Cohen’s D was 0.48 for training amount and 0.32 for confidence level, suggesting medium and weak-medium sized associations to work experience, respectively. Conclusions: ECHR work experience is common among medical students, and was associated with more training and higher confidence in the investigated procedures. Significant associations were also seen between training and confidence, and year of study, previous healthcare-related education and TAMS participation, but military medic-training showed no association.

BackgroundThe COVID-19 pandemic has impacted many facets of life. This study focuses on undergraduate and postgraduate students in China to explore how the pandemic has affected health status, daily life, learning situations, graduation-related situations, and their studies or work planning.MethodsThis study sent online questionnaires to 2,395 participants to investigate the extent to which they were affected by the epidemic in the various aspects mentioned above and to understand what help they tend to get in the face of these effects.ResultsA total of 2,000 valid questionnaires were collected. The physical health of 82.90% of the respondents was affected to varying degrees, with male students, non-medical students, and graduates being more affected than female students, students with medical majors, and non-graduates, respectively. The proportion of students affected by mental health, the total amount of physical exercise, emotional life, and interpersonal communication was 86.35, 88.65, 80.15, and 90.15%, respectively. Compared with medical students and non-graduates, non-medical students and graduates were more affected. In addition, students’ learning and graduation conditions have also been affected to a certain extent: 13.07% of students may not be able to graduate on time, and the proportion of postgraduate students’ graduations affected was higher than that of undergraduate students.ConclusionThe COVID-19 pandemic has affected the health status of students, their daily lives, learning situations, and so on to varying degrees. We need to pay attention to the issues, provide practical solutions, and provide a basis for better responses to similar epidemics in the future.

This data set shows the number and percentage of children graduating from high school in Travis County, including public, private, charter, home schools, and other high school equivalents. The data is from the Texas Education Agency (TEA) state agency that oversees primary and secondary public education in the state of Texas. View county-level data: https://data.austintexas.gov/Health-and-Community-Services/Strategic-Measure_Percentage-of-Students-Graduatin/djfu-26dw View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/n78t-2him

This dataset comprises physician-level entries from the 1906 American Medical Directory, the first in a series of semi-annual directories of all practicing physicians published by the American Medical Association [1]. Physicians are consistently listed by city, county, and state. Most records also include details about the place and date of medical training. From 1906-1940, Directories also identified the race of black physicians [2].This dataset comprises physician entries for a subset of US states and the District of Columbia, including all of the South and several adjacent states (Alabama, Arkansas, Delaware, Florida, Georgia, Kansas, Kentucky, Louisiana, Maryland, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia). Records were extracted via manual double-entry by professional data management company [3], and place names were matched to latitude/longitude coordinates. The main source for geolocating physician entries was the US Census. Historical Census records were sourced from IPUMS National Historical Geographic Information System [4]. Additionally, a public database of historical US Post Office locations was used to match locations that could not be found using Census records [5]. Fuzzy matching algorithms were also used to match misspelled place or county names [6].The source of geocoding match is described in the “match.source” field (Type of spatial match (census_YEAR = match to NHGIS census place-county-state for given year; census_fuzzy_YEAR = matched to NHGIS place-county-state with fuzzy matching algorithm; dc = matched to centroid for Washington, DC; post_places = place-county-state matched to Blevins & Helbock's post office dataset; post_fuzzy = matched to post office dataset with fuzzy matching algorithm; post_simp = place/state matched to post office dataset; post_confimed_missing = post office dataset confirms place and county, but could not find coordinates; osm = matched using Open Street Map geocoder; hand-match = matched by research assistants reviewing web archival sources; unmatched/hand_match_missing = place coordinates could not be found). For records where place names could not be matched, but county names could, coordinates for county centroids were used. Overall, 40,964 records were matched to places (match.type=place_point) and 931 to county centroids ( match.type=county_centroid); 76 records could not be matched (match.type=NA).Most records include information about the physician’s medical training, including the year of graduation and a code linking to a school. A key to these codes is given on Directory pages 26-27, and at the beginning of each state’s section [1]. The OSM geocoder was used to assign coordinates to each school by its listed location. Straight-line distances between physicians’ place of training and practice were calculated using the sf package in R [7], and are given in the “school.dist.km” field. Additionally, the Directory identified a handful of schools that were “fraudulent” (school.fraudulent=1), and institutions set up to train black physicians (school.black=1).AMA identified black physicians in the directory with the signifier “(col.)” following the physician’s name (race.black=1). Additionally, a number of physicians attended schools identified by AMA as serving black students, but were not otherwise identified as black; thus an expanded racial identifier was generated to identify black physicians (race.black.prob=1), including physicians who attended these schools and those directly identified (race.black=1).Approximately 10% of dataset entries were audited by trained research assistants, in addition to 100% of black physician entries. These audits demonstrated a high degree of accuracy between the original Directory and extracted records. Still, given the complexity of matching across multiple archival sources, it is possible that some errors remain; any identified errors will be periodically rectified in the dataset, with a log kept of these updates.For further information about this dataset, or to report errors, please contact Dr Ben Chrisinger (Benjamin.Chrisinger@tufts.edu). Future updates to this dataset, including additional states and Directory years, will be posted here: https://dataverse.harvard.edu/dataverse/amd.References:1. American Medical Association, 1906. American Medical Directory. American Medical Association, Chicago. Retrieved from: https://catalog.hathitrust.org/Record/000543547.2. Baker, Robert B., Harriet A. Washington, Ololade Olakanmi, Todd L. Savitt, Elizabeth A. Jacobs, Eddie Hoover, and Matthew K. Wynia. "African American physicians and organized medicine, 1846-1968: origins of a racial divide." JAMA 300, no. 3 (2008): 306-313. doi:10.1001/jama.300.3.306.3. GABS Research Consult Limited Company, https://www.gabsrcl.com.4. Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 17.0 [GNIS, TIGER/Line & Census Maps for US Places and Counties: 1900, 1910, 1920, 1930, 1940, 1950; 1910_cPHA: ds37]. Minneapolis, MN: IPUMS. 2022. http://doi.org/10.18128/D050.V17.05. Blevins, Cameron; Helbock, Richard W., 2021, "US Post Offices", https://doi.org/10.7910/DVN/NUKCNA, Harvard Dataverse, V1, UNF:6:8ROmiI5/4qA8jHrt62PpyA== [fileUNF]6. fedmatch: Fast, Flexible, and User-Friendly Record Linkage Methods. https://cran.r-project.org/web/packages/fedmatch/index.html7. sf: Simple Features for R. https://cran.r-project.org/web/packages/sf/index.html

This table provides an overview of the key figures on health and care available on StatLine. All figures are taken from other tables on StatLine, either directly or through a simple conversion. In the original tables, breakdowns by characteristics of individuals or other variables are possible. The period after the year of review before data become available differs between the data series. The number of exam passes/graduates in year t is the number of persons who obtained a diploma in school/study year starting in t-1 and ending in t.

Data available from: 2001

Status of the figures:

2024: Most available figures are definite. Figures are provisional for: - causes of death; - youth care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university).

2023: Most available figures are definite. Figures are provisional for: - perinatal mortality at pregnancy duration at least 24 weeks; - diagnoses known to the general practitioner; - hospital admissions by some diagnoses; - average period of hospitalisation; - supplied drugs; - AWBZ/Wlz-funded long term care; - physicians and nurses employed in care; - persons employed in health and welfare; - average distance to facilities; - profitability and operating results at institutions. Figures are revised provisional for: - expenditures on health and welfare.

2022: Most available figures are definite. Figures are revised provisional for: - expenditures on health and welfare.

2021: Most available figures are definite, Figures are revised provisional for: - expenditures on health and welfare.f

2020 and earlier: All available figures are definite.

Changes as of 4 July 2025: More recent figures have been added for: - causes of death; - life expectancy; - life expectancy in perceived good health; - self-perceived health; - hospital admissions by some diagnoses; - sickness absence; - average period of hospitalisation; - contacts with health professionals; - youth care; - smoking, heavy drinkers, physical activity; - overweight; - high blood pressure; - physicians and nurses employed in care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university); - expenditures on health and welfare; - profitability and operating results at institutions.

Changes as of 18 december 2024: - Distance to facilities: the figures withdrawn on 5 June have been replaced (unchanged). - Youth care: the previously published final results for 2021 and 2022 have been adjusted due to improvements in the processing. - Due to a revision of the statistics Expenditure on health and welfare 2021, figures for expenditure on health and welfare care have been replaced from 2021 onwards. - Due to the revision of the National Accounts, the figures on persons employed in health and welfare have been replaced for all years. - AWBZ/Wlz-funded long term care: from 2015, the series Wlz residential care including total package at home has been replaced by total Wlz care. This series fits better with the chosen demarcation of indications for Wlz care.

When will new figures be published? New figures will be published in December 2025.

This dataset comprises micro-ultrasound scans and human prostate annotations of 75 patients who underwent micro-ultrasound guided prostate biopsy at the University of Florida. All images and segmentations have been fully de-identified in the NIFTI format.

Under the "train" folder, you'll find three subfolders:

1. "micro_ultrasound_scans" contains micro-ultrasound images from 55 patients for training.
2. "expert_annotations" contains ground truth prostate segmentations annotated by our expert urologist.
3. "non_expert_annotations" contains prostate segmentations annotated by a graduate student.

In the "test" folder, there are five subfolders:

1. "micro_ultrasound_scans" contains micro-ultrasound images from 20 patients for testing.
2. "expert_annotations" contains ground truth prostate segmentations by the expert urologist.
3. "master_student_annotations" contains segmentations by a master's student.
4. "medical_student_annotations" contains segmentations by a medical student.
5. "clinician_annotations" contains segmentations by a urologist with limited experience in reading micro-ultrasound images.

If you use this dataset, please cite our paper: Jiang, Hongxu, et al. "MicroSegNet: A deep learning approach for prostate segmentation on micro-ultrasound images." Computerized Medical Imaging and Graphics (2024): 102326. DOI: https://doi.org/10.1016/j.compmedimag.2024.102326.

For any dataset-related queries, please reach out to Dr. Wei Shao: weishao@ufl.edu.

"""Local Law 14 (2016) requires that the NYCDOE provide citywide Health Education data, dis aggregated by community school district, city council district, and each individual school. Data reported in this report is from the 2015-16 school year. "" This report provides information about the number and percent of students receiving one semester of health education as defined in Local Law 14 as reported through the 2015-2016 STARS database. It is important to note that schools self-report their scheduling information in STARS.

This report consists of 10 tabs:

1. Health Education Standards
2. HS - School
3. HS-District
4. JS-City Council District
5. MS-School
6. MS-District
7. Ms-City Council District
8. Efficacy
9. Compliance

10. LGBTQ Inclusivity

11. Health Education Standards

This tab provides information on the New York State Health Education Requirements and Standards. These requirements can be found in NYS Education Commissioner’s Regulation Subchapter G Part 135.

1. HS - School

This tab includes school level data on the number of students that received a semester (one credit) of health instruction, as well as the number of June and August graduates meeting the HS health requirements for the 2015-2016 school year. Note that students are not required to receive health instruction at any particular grade level in high school, only prior to graduating. Additionally, values less than 100% do not necessarily imply that students graduated without meeting credit requirements. In very rare cases, these values may indicate missing or incomplete historical transcript data.

1. HS - District

This tab includes district level data on the number of students that received a semester (one credit) of health instruction, as well as the number of June and August graduates meeting the HS health requirements for the 2015-2016 school year. Note that students are not required to receive health instruction at any particular grade level in high school, only prior to graduating. Additionally, values less than 100% do not necessarily imply that students graduated without meeting credit requirements. In very rare cases, these values may indicate missing or incomplete historical transcript data.

1. HS - City Council District

This tab includes city council district level data on the number of students that received a semester (one credit) of health instruction, as well as the number of June and August graduates meeting the HS health requirements for the 2015-2016 school year. Note that students are not required to receive health instruction at any particular grade level in high school, only prior to graduating. Additionally, values less than 100% do not necessarily imply that students graduated without meeting credit requirements. In very rare cases, these values may indicate missing or incomplete historical transcript data.

1. Ms - School

This tab includes school level data on the number of 6-8 graders that received a semester (one half-unit) of health instruction, as well as the number of 8th graders meeting the middle school health requirements for the 2015-2016 school year. Note that this regulation does not require students to receive health instruction at any particular grade level in middle school, only prior to completing 8th grade. However, a student may advance to the next grade without completing the course.

1. MS - District

This tab includes district level data on the number of 6-8 graders that received a semester (one half-unit) of health instruction, as well as the number of 8th graders meeting the middle school health requirements for the 2015-2016 school year. Note that this regulation does not require students to receive health instruction at any particular grade level in middle school, only prior to completing 8th grade. However, a student may advance to the next grade without completing the course.

1. MS - City Council District

This tab includes Cit

ObjectiveTo evaluate the prevalence and possible factors associated with the development of burnout among medical students in the first years of undergraduate school.MethodA cross-sectional study was conducted at the Barretos School of Health Sciences, Dr. Paulo Prata. A total of 330 students in the first four years of medical undergraduate school were invited to participate in responding to the sociodemographic and Maslach Burnout Inventory-Student Survey (MBI-SS) questionnaires. The first-year group consisted of 150 students, followed by the second-, third-, and fourth-year groups, with 60 students each.ResultsData from 265 students who answered at least the sociodemographic questionnaire and the MBI-SS were analyzed (response rate = 80.3%). One (n = 1, 0.3%) potential participant viewed the Informed Consent Form but did not agree to participate in the study. A total of 187 students (187/265, 70.6%) presented high levels of emotional exhaustion, 140 (140/265, 52.8%) had high cynicism, and 129 (129/265, 48.7%) had low academic efficacy. The two-dimensional criterion indicated that 119 (44.9%) students experienced burnout. Based on the three-dimensional criterion, 70 students (26.4%) presented with burnout. The year with the highest frequency of affected students for both criteria was the first year (p = 0.001). Personal attributes were able to explain 11% (ΔR = 0.11) of the variability of burnout under the two-dimensional criterion and 14.4% (R2 = 0.144) under the three-dimensional criterion.ConclusionThis study showed a high prevalence of burnout among medical students in a private school using active teaching methodologies. In the first years of graduation, students’ personal attributes (optimism and self-perception of health) and school attributes (motivation and routine of the exhaustive study) were associated with higher levels of burnout. These findings reinforce the need to establish preventive measures focused on the personal attributes of first-year students, providing better performance, motivation, optimism, and empathy in the subsequent stages of the course.

Detailed Dataset Description

This dataset is a comprehensive collection of [type of data, e.g., financial, medical, demographic, or environmental] information spanning the period from [start date] to [end date]. It is designed to provide insights into [main purpose of the dataset, e.g., market trends, patient behavior, climate patterns, consumer habits] and is suitable for advanced analysis, predictive modeling, and visualization.

Contents

Number of Records: [total rows]

Number of Features/Columns: [total columns]

Feature Types: Includes both numeric and categorical features, such as [examples of numeric features], [examples of categorical features], and [any special types, e.g., time series, text data].

Target Variable (if applicable): [Name of the target variable, e.g., “Gold Price”, “Insurance Charges”, “Customer Purchase Amount”]

Missing Values & Data Quality: [Brief note about missing values, anomalies, or cleaning required]

Context This dataset captures [real-world context], enabling users to explore patterns, correlations, and trends. Analysts and data scientists can use it for tasks such as:

Statistical analysis and reporting

Machine learning modeling and predictive analytics

Trend visualization and forecasting

Correlation and causal relationship studies

Additional Notes

The data is collected from [sources, e.g., public records, surveys, APIs, financial exchanges].

It has undergone [brief description of preprocessing, cleaning, or normalization if any].

The dataset is intended for [specific audience, e.g., researchers, data analysts, business strategists] and can be integrated into [specific tools or platforms if relevant].

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated on 8/14/2024

High School Graduation Rate - This indicator shows the percentage of students who graduate high school in four years. Completion of high school is one of the strongest predictors of health in later life. People who graduate from high school are more likely to have better health outcomes, regularly visit doctors, and live longer than those without high school diplomas. https://health.maryland.gov/pophealth/Documents/SHIP/SHIP%20Lite%20Data%20Details/High%20School%20Graduation%20Rate.pdf" > Link to Data Details

Objective Usage of autotext or “dotphrases†is ubiquitous among provider workflows in electronic health records (EHRs). Yet little is known about the impact of these tools in inpatient settings and among resident physicians. We aimed to evaluate the association between autotext usage and documentation time among resident physicians in an academic medical center using the Cerner® EHR. Dataset Description The association between auto text executions and documentation time per patient seen for 705 resident physicians rotating at a large academic medical center from July 2021 to June 2023 was analyzed via linear regression after controlling for specialty, post-graduate year (PGY), provider gender, and patient volume. NOTE: The dataset in this study cannot be shared publicly due to the risk of identifying subjects who constitute a vulnerable population and may be known personally to members of the research community (physicians in training). Inclusion of details of gender, department, and ye..., , # Does autotext usage decrease documentation time among resident physicians? A retrospective analysis of EHR usage Data

Dataset DOI: 10.5061/dryad.xksn02vt5

Description of the data and file structure

Short synthetic sample intended to mimic data used in retrospective study of autotext usage and documentation time among resident physicians. A synthetic dataset is presented because the actual dataset used in this study could not be sufficiently anonymized to share publicly due to the nature of the data (see Abstract.)

The dataset originally used in this study was prepared from raw data downloaded from the Cerner LightsOn and Cerner Advance toolkits and aggregated at the level of an individual resident physician over the course of an academic year. As the study covers two academic years, 2021-2022 and 2022-2023, there are two entries for some resident physicians who were at the institution during both academic years. The dataset includes a randomly gene...,

HPAT excelExcel file of data input for study. This data set is an excel sheet of all the students who answered the JSPE questionnaire with stated empathy score aligned with their corresponding gender, age, year of study, HPAT results (total HPAT, subsection 1,2 and 3).

** Description**

This dataset contains data about lung cancer Mortality and is a comprehensive collection of patient information, specifically focused on individuals diagnosed with cancer. This dataset contains comprehensive information on 800,000 individuals related to lung cancer diagnosis, treatment, and outcomes. With 16 well-structured columns. This large-scale dataset is designed to aid researchers, data scientists, and healthcare professionals in studying patterns, building predictive models, and enhancing early detection and treatment strategies.

🌍 The Societal Impact of Lung Cancer

Lung cancer is not just a disease — it's a global crisis that steals time, health, and hope from millions of people every year. As the #1 cause of cancer deaths worldwide, it takes more lives annually than breast, colon, and prostate cancer combined.

But behind every statistic is a story:

A parent who never saw their child graduate.

A worker who had to leave their job too soon.

A community that lost a leader, a friend, a neighbor.

Why does this matter? Lung cancer often goes undetected until it's too late. It’s aggressive, silent, and devastating — especially in underserved areas where early detection is rare and treatment options are limited. It doesn’t just affect patients. It affects families, economies, and healthcare systems on a massive scale.

This dataset represents more than numbers. It represents 800,000 real-world stories — people who can help us unlock patterns, train models, and advance life-saving research.

By working with this data, you're not just analyzing a dataset — you're stepping into the fight against one of humanity’s deadliest diseases.

Let’s turn insight into impact. (😊The above descriptions is generated with the help of AI, Just wanted to share this dataset That all. Thank you)

This dataset contains the official 2024 NIRF (National Institutional Ranking Framework) rankings of higher education institutions in India. The NIRF rankings are released annually by the Ministry of Education, Government of India, and provide a comprehensive assessment of the top institutions based on parameters such as teaching, learning, research, graduation outcomes, and more.

Key Features: Year: 2024 NIRF Rankings Categories: This dataset includes rankings across various fields, including Engineering, Medical, Management, Law, and others. Ranking Parameters: The dataset captures key ranking criteria, such as: - Teaching, Learning & Resources - Research & Professional Practices - Graduation Outcomes - Outreach & Inclusivity - Perception Columns: Institution Id: Id of the college/university. Institution Name: Name of the college/university. TLR: Teaching, Learning & Resources RPC: Research, Professional Practice, and Collaborative Performance GO: Graduation Outcomes OI: Outreach & Inclusivity Perception: Perception City: Location of the institution. State: State in which the institution is located. Score: Overall NIRF score out of 100. Rank: The position of the institution in the NIRF 2024 ranking. Field: Specific ranking field (e.g., Engineering, Medical, etc). Use Cases: Academic Research: Analyze trends in the ranking of educational institutions over time. Higher Education Guidance: Help students and parents make informed decisions about college selection. Data Analysis: Perform data analytics or visualizations to explore the performance of institutions based on NIRF parameters. Source: The data is sourced from the official NIRF website.

ABSTRACT The objective of this study is to evaluate the impact of training in online searching in Medical Literature Analysis and Retrieval System Online (MedLine) and Biblioteca Virtual em Saúde (BVS) Research Portal databases, on the information seeking behavior of graduate students and residents of the Health Science Campus of the Federal University of Minas Gerais. The research used the Kirkpatrick model for evaluating training specifically focusing on first and second levels of this model. The basic descriptive research used a quantitative approach. The research used a non-random sample consisting of the graduate students and residents who agreed to participate in the course. This training had a course load of 15 hours and was offered to postgraduate students of the Graduate Campus Health of the Federal University of Minas Gerais and residents of Obstetric Nursing Residency Program of the Federal University of Minas Gerais School of Nursing courses. Data collection was conducted using two questionnaires at the beginning and at the end of the training. We compared the answers given by the respondents in these questionnaires. The results showed that the training had a positive impact on the informational seeking behavior because students have acquired new knowledge and research skills.

Note: CoP, WHO Global Code of Practice on the International Recruitment of Health Personnel; US IMGs, citizens of the US who graduated from non-US medical schools; non-US IMGs, foreign nationals who graduated from non-US medical schools.Data sources: Educational Commission for Foreign Medical Graduates [27–30].Numbers and percentages of US and non-US citizens who graduated from international medical schools in the National Residency Match Program after the 2010 launch of the CoP.

The Institutional Data Archive on American Higher Education (IDA) contains academic data on 384 four-year colleges and universities in the United States. The IDA is one of two databases produced by the Colleges and Universities 2000 project based at the University of California, Riverside. This release, the third compilation of the IDA, is updated through academic year 2010-2011, and includes longitudinal and cross-sectional data from multiple sources. The collection is organized into nine datasets based on the unit of analysis and whether identifiers linking the data to particular institutions are present; seven of the datasets can be linked by a common identifier variable (PROJ_ID), and two cannot be linked due to confidentiality agreements. The seven identifiable datasets contain information on institutions, university systems, programs and academic departments, earned degrees, graduate schools, medical schools, and institutional academic rankings over time. Data regarding student enrollments, average SAT and ACT scores, and tuition and fees has been recorded, as well as institutional information concerning libraries, research activity, revenue and expenditures, faculty salaries, and quality rankings for program faculty. The identifiable datasets also include census information for neighborhoods surrounding IDA colleges and universities. The two non-identifiable datasets contain confidential survey responses from IDA institution presidents, chancellors, provosts, and academic vice presidents; survey questions pertained to governance structures, institutional goals and achievements, and solicited opinions on current and future issues facing the respondent's institution and higher education in general.

Этот набор данных содержит данные о выпускниках в разбивке по категориям, определенным следующим образом: Выпускники медицинских вузов: количество студентов, окончивших медицинские факультеты или аналогичные учебные заведения по специальности "медицина", т.е. получивших базовое медицинское образование в данном году. Выпускники стоматологов: количество студентов, получивших признанную квалификацию в области стоматологии в данном году. Выпускники фармацевтов: количество студентов, получивших признанную квалификацию в области фармации в данном году. Выпускники-акушерки: количество студентов, получивших признанную квалификацию в области акушерства в данном году. Выпускники-медсестры: количество студентов, получивших признанную квалификацию в области сестринского дела в данном году. Выпускники профессиональных медицинских сестер: количество студентов, получивших признанную квалификацию профессиональной медсестры в данном году. Выпускники младших профессиональных медицинских сестер: количество студентов, получивших признанную квалификацию младшей профессиональной медсестры в данном году. This dataset provides data on graduates by category defined as follows: Medical graduates: number of students who have graduated in medicine from medical faculties or similar institutions, i.e., who have completed basic medical education in a given year. Dentists graduates: number of students who have obtained a recognised qualification in dentistry in a given year. Pharmacists graduates: number of students who have obtained a recognised qualification in pharmacy in a given year. Midwives graduates: number of students who have obtained a recognised qualification in midwifery in a given year. Nursing graduates: number of students who have obtained a recognised qualification in nursing in a given year. Professional nursing graduates: number of students who have obtained a recognised qualification as a professional nurse in a given year. Associate professional nursing graduates: number of students who have obtained a recognised qualification as an associate professional nurse in a given year.