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.

Data Description The data presented is from a survey that investigated the usage of information and communication technologies (ICT) for eLearning amongst the 2017 medical student population at Wits. Methods The methodology was a descriptive, cross-sectional, online and paper-based survey. It was distributed to a convenience sample of medical students at Wits. The survey was generated using REDCap (Research Electronic Data Capture) software. The target population was stratified by points in the curriculum in which there is a change due to the teaching and learning methodology being used or the addition of new students into the class. 1 First year (entry year; n=255) 2 Third year (when graduate entrants join the school leavers in the Graduate Entry Medical Programme (GEMP); n=350) 3 Sixth year (final year; n=319) medical students. Process A pilot study with 19 student volunteers was conducted starting in May 2017. Volunteers were recruited by students from MBBCh 5. Following the pilot study, the questionnaire was edited to reduce the length, enhance clarity and to ensure readability across a range of devices. The final survey consisted of seven sections: 1. information and consent (1 question), 2. demographic data (4 questions), 3. year of study (2 questions), 4. device ownership and 5. usage to support learning (12 questions), 6. access to and reliability of the internet connection (5 questions), 7. usage of the learning management system (2 questions), 8. BYOD (6 questions). In Section 4, students were also asked to place themselves on a 100-point scale bound by opposite terms designed to measure their attitude and disposition and attitude to technology as developed and validated in the ECAR study. Lower numbers indicate certain characteristics about disposition to use technology (reluctant user, late adopter, critic, technophobe) and attitudes towards technology usage (useless, burdensome, distraction), while higher numbers indicate more positive dispositions (enthusiast, supporter, early adopter, technophile) and attitudes (useful, beneficial, enhancement) towards ICT.

Medical Doctors in Turkey increased to 2.18 per 1000 people in 2021 from 2.05 per 1000 people in 2020. This dataset includes a chart with historical data for Turkey Medical Doctors.

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

Forecast: Number of Medical Doctors Graduates in France 2024 - 2028 Discover more data with ReportLinker!

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. Maryland has 200+ higher education facilities located throughout the entire State. Maryland boasts a highly educated workforce with 300 - 000+ graduates from higher education institutions every year. Higher education opportunities range from two year - public and private institutions - four year - public and private institutions and regional education centers. Collectively - Maryland's higher education facilities offer every kind of educational experience - whether for the traditional college students or for students who have already begun a career and are working to learn new skills. Maryland is proud that nearly one-third of its residents 25 and older have a bachelor's degree or higher - ranking in the top 5 amongst all states. Maryland's economic diversity and educational vitality is what makes it one of the best states in the nation in which to live - learn - work and raise a family. Last Updated: 06/2013 Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/Education/MD_EducationFacilities/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

The Distribution Priority Area (DPA) classification system for GPs is a mechanism used by the Government to encourage a more equitable distribution of GPs who are restricted under s19AB of the Health Insurance Act 1973, including International Medical Graduates (IMGs) and Foreign Graduates of Accredited Medical Schools (FGAMS).\r \r • The DPA classification system is also used to distribute Australian-trained Bonded doctors who have a return of service obligation.\r • The Distribution Priority Area (DPA) replaced the Districts of Workforce Shortage (DWS) system for GPs in 2019. DWS remains in use for non-GP specialists.\r • DPA considers the demographics of a region, including age, gender and socio-economic groupings, along with MBS activity data, to determine a benchmark figure that reflects community need for GP service.\r • DPA is calculated for 824 distinct, non-overlapping GP catchments throughout Australia. Catchments assessed below the benchmark are classified DPA.\r • Areas classified MM 2-7 under the Modified Monash Model (MMM) geographical remoteness classification system are automatically DPA. MM1 Inner metropolitan locations are automatically non-DPA. Areas that held DPA status prior to this update will continue to hold DPA status under a No Losers policy.\r \r The attached file for the DPA is as at March 2025 (most recent annual update). As the files may be updated at short notice for program purposes, please ensure you use the Department of Health and Aged Care Health Workforce Locator (https://www.health.gov.au/resources/apps-and-tools/health-workforce-locator) for the most up-to-date and official DPA location status.\r

The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

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).

With the recent Ebola epidemic, the flaws in Liberia’s medical infrastructure have been made painfully obvious. Liberia, a country of four million people, has only 37 practicing doctors according to health officials. This is evidence of a serious lack in the availability of medical services to the majority of Liberians. An American gynecologist who visited the country in 2012 to provide services with a team from the Mt. Sinai Hospital observed families of hospital patients supplying their own food and bed linens due to the medical facility they were working in lacking funds for basic necessities. The root issue at the heart of many of Liberia’s woes stems from the long civil war. In addition to damaging the medical infrastructure, the country’s only medical school was forced to close for long periods of time, resulting in medical students taking an average eight years to graduate. There has been a serious push for reform and revitalization with medical facilities being rebuilt and medical students now on track to spend only three years in school. Liberia is facing a number of issues, and prior to the current epidemic has not prioritized health expenditures. The government spends an estimated 16.8 percent of their GDP, the lowest in the world, on healthcare. The average GDP spending on healthcare systems in sub-Saharan Africa is ~50 percent. Liberia’s healthcare system is highly dependent on international aid. Donors finance 50 percent of total health expenditures. Approximately 80 percent of all health services are provided by non-governmental organizations (NGOs) and will continue to be so for the foreseeable future. However, the Ministry of Health and Social Welfare has been working with NGOs such as Health Systems 20/20 to improve their existing infrastructure. Attribute Table Field DescriptionsISO3 - International Organization for Standardization 3-digit country code ADM0_NAME - Administration level zero identification / name ADM1_NAME - Administration level one identification / name ADM2_NAME - Administration level two identification / name NAME - Name of health facility TYPE1 - Primary classification in the geodatabase TYPE2 - Secondary classification in the geodatabase CITY - City location available SPA_ACC - Spatial accuracy of site location (1 – high, 2 – medium, 3 – low) COMMENTS - Comments or notes regarding themedical facility SOURCE_DT - Source one creation date SOURCE - Source one SOURCE2_DT - Source two creation date SOURCE2 - Source two CollectionThe feature class was generated utilizing data from OpenStreetMap, Wikimapia, GeoNames and other sources. OpenStreetMap is a free worldwide map, created by crowd-sourcing. Wikimapia is open-content mapping focused on gathering all geographical objects in the world. GeoNames is a geographical places database maintained and edited by the online community. Consistent naming conventions for geographic locations were attempted but name variants may exist, which can include historical or less widespread interpretations.The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe is not responsible for the accuracy and completeness of data compiled from outside sources.Sources (HGIS)Aizenman, Nurith and Beemsterboer, Nicole. “Why Patients Aren’t Coming to Liberia’s Redemption Hospital.” August 27, 2014. Accessed September 26, 2014. www.npr.org.“Liberia: ArcelorMittal Folds Partly – Terminates Expansion Contract.” All Africa. August 14, 2013. Accessed September 26, 2014. allafrica.com. Cohen, Elizabeth. “Ebola Patients Left to Lie on the Ground.” CNN. September 23, 2014. Accessed September 26, 2014. www.cnn.com.“Kingdom Care Medical Center Reaches Rural Communities with Health Care.” Daily Observer. January 28, 2014. Accessed September 26, 2014. www.liberianobserver.com. DigitalGlobe, "DigitalGlobe Imagery Archive." Accessed September 24, 2014.“Eternal Love Winning Africa: ELWA Hospital.” Eternal Love Winning Africa. January 2014. Accessed September 26, 2014. www.elwaministries.org.Freeman, Colin. “One Patient in a 200-bed Hospital: How Ebola has Devastated Liberia’s Health System.” The Telegraph. August 15, 2014. Accessed September 26, 2014. www.telegraph.co.uk.“Lewin Reaches Out to River Gee, Maryland.” Gale Global Issues. March 4, 2013. . Accessed September 26, 2014. find.galegroup.com. Gbelewala, Korboi. “Liberia: Health Offical – Ebola Death Toll Hits 11 in Lofa.” All Africa. June 24, 2014. Accessed September 26, 2014. allafrica.com. GeoNames, "Liberia." September 23, 2014. Accessed September 23, 2014. www.geonames.org.Google, September 2014. Accessed September 2014. www.google.com.Kollie, Namotee P.M. “Liberia: C.B. Dunbar Hospital Receives Medical Supplies.” September 27, 2013. Accessed September 26, 2014. allafrica.com.“MSF Hands Over Last Hospitals to Ministry of Health after 20 Years of Emergency Aid in Liberia.” Medecins Sans Frontieres. June 25, 2010. Accessed September 26, 2014. www.msf.org. Nah, Vivian M. and Johnson, Obediah. “Liberia: Ebola Kills Woman at Duside Hospital in Firestone.” All Africa. April 4, 2014. Accessed September 26, 2014. allafrica.com. “Catholic Hospital Director Dies of Ebola in Liberia.” National Catholic Register. August 05, 2014. Accessed September 26, 2014. www.ncregister.com.OpenStreetMap, "Liberia." September 2014. Accessed September 18, 2014. www.openstreetmap.org.Senkpeni, Alpha Daffae. “No Ebola Gears for Clinic in Grand Bassa District #2.” Front Page Africa. August 12, 2014. Accessed September 26, 2014. www.frontpageafricaonline.com. “Seventh-day Adventist Cooper Hospital” Seventh-Day Adventist Church. November 18, 2004. Accessed September 26, 2014. www.adventistdirectory.org.“St. Benedict Menni Rehabilitation Centre, Liberia.” Sisters Hospitallers. January 2014. Accessed September 26, 2014. www.sistershospitallers.org. “Liberia – SOS Medical and Social Centres.” SOS Children’s Villages. January 2014. Accessed September 26, 2014. www.sos-medical-centres.org.“Liberia.” Sustainable Marketplace. January 2014. Accessed September 26, 2014. liberia.buildingmarkets.org. “Reconstruction of the Vinjama Hospital in Liberia.” Swiss Agency for Development and Cooperation (SDC). January 2014. Accessed September 26, 2014. www.sdc.admin.ch. Verdier, Lewis S. “Liberia: TB On the Rise in Pleebo.” All Africa. March 28, 2013. Accessed September 26, 2014. allafrica.com.Wikimapia, "Liberia." September 2014. Accessed September 22, 2014. wikimapia.org.“Snapper Hill Clinic.” Word Press. November 12, 2012. Accessed September 26, 2014. jbloodnc.wordpress.com.Sources (Metadata)Neporent, Liz. "Liberia's Medical Conditions Dire Even Before Ebola Outbreak." ABC News. August 4, 2014. Accessed October 3, 2014. abcnews.go.com."Liberia." Health Systems Strengthening: Where We Work:. January 1, 2014. Accessed October 3, 2014. www.healthsystems2020.org."Financing Liberia's Health Care." Health Systems Strengthening: News:. February 13, 2012. Accessed October 3, 2014. www.healthsystems2020.org.UNCLASSIFIED

High school graduate (includes equivalency) Poverty Rate Statistics for 2022. This is part of a larger dataset covering poverty in Medical Lake, Washington by age, education, race, gender, work experience and more.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. Maryland has 200+ higher education facilities located throughout the entire State. Maryland boasts a highly educated workforce with 300 - 000+ graduates from higher education institutions every year. Higher education opportunities range from two year - public and private institutions - four year - public and private institutions and regional education centers. Collectively - Maryland's higher education facilities offer every kind of educational experience - whether for the traditional college students or for students who have already begun a career and are working to learn new skills. Maryland is proud that nearly one-third of its residents 25 and older have a bachelor's degree or higher - ranking in the top 5 amongst all states. Maryland's economic diversity and educational vitality is what makes it one of the best states in the nation in which to live - learn - work and raise a family. Last Updated: 06/2013 Feature Service Layer Link: http://geodata.md.gov/imap/rest/services/Education/MD_EducationFacilities/FeatureServer/3 ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

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:

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

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

"micro_ultrasound_scans" contains micro-ultrasound images from 20 patients for testing. "expert_annotations" contains ground truth prostate segmentations by the expert urologist. "master_student_annotations" contains segmentations by a master's student. "medical_student_annotations" contains segmentations by a medical student. "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.

This dataset is a scraped collection of data from a Czech Statistical Institute website tracking course of studies at public and private universities. In the data we can see for which university, faculty, study program and start year we can see what were the counts of students during upcoming years.

I believe those data can be used to determine which study programs or universities are the most difficult to finish in standard time. I also wonder if any interesting facts are hidden in the data.

Source: https://statis.msmt.cz/statistikyvs/prubeh.aspx

Maryland has 200+ higher education facilities located throughout the entire State. Maryland boasts a highly educated workforce with 300,000+ graduates from higher education institutions every year. Higher education opportunities range from two year, public and private institutions, four year, public and private institutions and regional education centers. Collectively, Maryland's higher education facilities offer every kind of educational experience, whether for the traditional college students or for students who have already begun a career and are working to learn new skills. Maryland is proud that nearly one-third of its residents 25 and older have a bachelor's degree or higher, ranking in the top 5 amongst all states. Maryland's economic diversity and educational vitality is what makes it one of the best states in the nation in which to live, learn, work and raise a family.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/Education/MD_EducationFacilities/FeatureServer/3

Background: Physiotherapy is a kind of science that helps and supports the patient to live a healthy lifestyle. Physiotherapy working in India, the main source of reference is a medical practitioner. Physiotherapy is defined as a health care professionals dealing with human mobility and function maximizing the quality of one’s life and movement strength within the loop of prevention, promotion, treatment/intervention, habilitation, and rehabilitation. Still, there are people who aren’t aware of the kind of treatment it can provide. Hereby, the objective of this study is to know how much aware the medical practitioners are in terms of the importance and need for physiotherapy for the treatment of the patients. Materials and Methods: Apparently, an approved questionnaire was sent through a Google form link to 250 medical practitioners of Uttar Pradesh. 124 responses were received and analyzed. Out of 124, 71 of the respondents were female and 53 were male. All willing medical practitioners from different streams along with graduates and super specialists were included, whereas students and non-internet users were excluded. Result: From the study, it was learned that there is awareness regarding the term physiotherapy (), but specialization in physiotherapy is less known, maximum of the subjects were aware of specialization in orthopedics and specialization in women’s health, community-based rehabilitation and dermatology is least known. 79% of the medical practitioners have an objection in physiotherapist having the first contact with the patient. Conclusion: The study revealed that there is a lack of awareness regarding assessment and treatment protocol provided by physiotherapy. However, doctors believe physiotherapist has a big role in treating ICU and immobilized patients. There is less information regarding radiation modalities as well as recent advances in rehabilitation.

Historical Dataset of Pima Medical Institute-Seattle is provided by CommunityCollegeReview and contain statistics on metrics:Total Faculty Trends Over Years (2008-2023),Total Enrollment Trends Over Years (2007-2024),Student-Staff Ratio Trends Over Years (2008-2023),Full-Time Students Enrollment Trends Over Years (2007-2024),Full-Time Undergraduate Students Enrollment Trends Over Years (2007-2024),American Indian Student Percentage Comparison Over Years (2008-2024),Asian Student Percentage Comparison Over Years (2008-2023),Hawaiian Student Percentage Comparison Over Years (2018-2024),Hispanic Student Percentage Comparison Over Years (2007-2023),Black Student Percentage Comparison Over Years (2008-2023),White Student Percentage Comparison Over Years (2008-2023),Two or More Races Student Percentage Comparison Over Years (2010-2023),Non Resident Student Percentage Comparison Over Years (2019-2020),Diversity Score Comparison Over Years (2008-2023),Financial Aid Student Percentage Comparison Over the Years (2007-2023),Completion Rates For First-Time of Full-Time Students Comparison Over Years (2008-2024),Average Graduate Earnings (10 Years) Trends Over Years (2008-2013),Median Debt For Students Who Have Completed A Certificate Or Degree Trends Over Years (2008-2023),Median Debt For Students Who Have Not Completed A Certificate Or Degree Trends Over Years (2008-2023)

Historical Dataset of Pima Medical Institute-Chula Vista is provided by CommunityCollegeReview and contain statistics on metrics:Total Faculty Trends Over Years (2008-2023),Total Enrollment Trends Over Years (2007-2024),Student-Staff Ratio Trends Over Years (2008-2023),Full-Time Students Enrollment Trends Over Years (2007-2024),Full-Time Undergraduate Students Enrollment Trends Over Years (2007-2024),American Indian Student Percentage Comparison Over Years (2008-2024),Asian Student Percentage Comparison Over Years (2008-2023),Hawaiian Student Percentage Comparison Over Years (2012-2024),Hispanic Student Percentage Comparison Over Years (2007-2023),Black Student Percentage Comparison Over Years (2008-2023),White Student Percentage Comparison Over Years (2008-2023),Two or More Races Student Percentage Comparison Over Years (2009-2021),Non Resident Student Percentage Comparison Over Years (2019-2020),Diversity Score Comparison Over Years (2008-2023),Financial Aid Student Percentage Comparison Over the Years (2007-2023),Completion Rates For First-Time of Full-Time Students Comparison Over Years (2008-2024),Average Graduate Earnings (10 Years) Trends Over Years (2008-2013),Median Debt For Students Who Have Completed A Certificate Or Degree Trends Over Years (2008-2023),Median Debt For Students Who Have Not Completed A Certificate Or Degree Trends Over Years (2008-2023)

Historical Dataset of Pima Medical Institute-Denver is provided by CommunityCollegeReview and contain statistics on metrics:Total Faculty Trends Over Years (2008-2023),Total Enrollment Trends Over Years (2007-2024),Student-Staff Ratio Trends Over Years (2008-2023),Full-Time Students Enrollment Trends Over Years (2007-2024),Full-Time Undergraduate Students Enrollment Trends Over Years (2007-2024),American Indian Student Percentage Comparison Over Years (2008-2023),Asian Student Percentage Comparison Over Years (2008-2023),Hawaiian Student Percentage Comparison Over Years (2018-2024),Hispanic Student Percentage Comparison Over Years (2008-2022),Black Student Percentage Comparison Over Years (2008-2023),White Student Percentage Comparison Over Years (2008-2023),Two or More Races Student Percentage Comparison Over Years (2011-2023),Diversity Score Comparison Over Years (2008-2023),Financial Aid Student Percentage Comparison Over the Years (2007-2023),Completion Rates For First-Time of Full-Time Students Comparison Over Years (2008-2024),Average Graduate Earnings (10 Years) Trends Over Years (2008-2013),Median Debt For Students Who Have Completed A Certificate Or Degree Trends Over Years (2008-2023),Median Debt For Students Who Have Not Completed A Certificate Or Degree Trends Over Years (2008-2023)

This dataset tracks annual distribution of students across grade levels in Crane Medical Prep High School