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Student characteristics, professional preferences, and admission to medical school.

ABSTRACT: Objectives: A potential new avenue to address the shortage of country doctors is to change the rules for admission to medical school. We therefore study the link between high-school grade point average and prospective physicians' choice to work in rural areas. To further inform the discussion about rules for admission, we also study the effects of other predictors: a measure of students' attitudes towards risk; whether they waited for their place of study (Wartesemester); whether their parents worked as medical doctors; and whether they have some practical experience in the medical sector. Methods: We conducted two internet surveys in 2012 and 2014. In the first survey, the sample comprised 701 students and in the second, 474 students. In both surveys, we asked students for their regional preferences; in the 2014 survey, we additionally asked students for their first, second, and third preferences among a comprehensive set of specializations, including becoming a general practitioner. In both surveys, we asked students for basic demographic information (age and gender), their parents' occupation, a measure of subjective income expectations, a measure of risk attitudes, and their high-school grade point average (Abiturnote), and First National Boards Examination grade (Physikum). In 2014, we additionally asked for waiting periods (Wartesemester) as well as for prior professional experience in the health-care sector. Results: We find that three factors increase the probability of having a preference for working in a rural area significantly, holding constant all other influences: having a medical doctor among the parents, having worse grades in the high-school grade point average, and being more risk averse. Moreover, we find that those willing to work in the countryside have significantly more experience in the medical sector before admission to medical school. Discussion: Our results suggest that a change in the selection process for medical school may increase the supply of country doctors. Instead of focusing on the high-school grade point average, universities could even more intensely screen for study motivation through interviews or by taking into account students' background, extracurricular activities, or waiting periods.

SUBMITTER: Kesternich I

PROVIDER: S-EPMC5327662 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Student characteristics, professional preferences, and admission to medical school.

Kesternich Iris I Schumacher Heiner H Winter Joachim J Fischer Martin R MR Holzer Matthias M

GMS journal for medical education 20170215 1

<b>Objectives:</b> A potential new avenue to address the shortage of country doctors is to change the rules for admission to medical school. We therefore study the link between high-school grade point average and prospective physicians' choice to work in rural areas. To further inform the discussion about rules for admission, we also study the effects of other predictors: a measure of students' attitudes towards risk; whether they waited for their place of study (<i>Wartesemester</i>); whether t ...[more]

PMID: 28293672

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Project description:BackgroundDespite the growing number of patients with limited English proficiency in the United States, not all medical schools offer medical language courses to train future physicians in practicing language-concordant care. Little is known about the long-term use of non-English languages among physicians who took language courses in medical school. We conducted a cross-sectional study to characterize the professional language use of Harvard Medical School (HMS) alumni who took a medical language course at HMS and identify opportunities to improve the HMS Medical Language Program.MethodsBetween October and November 2019, we sent an electronic survey to 803 HMS alumni who took a medical language course at HMS between 1991 and 2019 and collected responses. The survey had questions about the language courses and language use in the professional setting. We analyzed the data using descriptive statistics and McNemar's test for comparing proportions with paired data. The study was determined not to constitute human subjects research.ResultsThe response rate was 26% (206/803). More than half of respondents (n = 118, 57%) cited their desire to use the language in their future careers as the motivation for taking the language courses. Twenty-eight (14%) respondents indicated a change from not proficient before taking the course to proficient at the time of survey whereas only one (0.5%) respondent changed from proficient to not proficient (McNemar's p-value < 0.0001). Respondents (n = 113, 56%) reported that clinical electives abroad influenced their cultural understanding of the local in-country population and their language proficiency. Only 13% (n = 27) of respondents have worked in a setting that required formal assessments of non-English language proficiency.ConclusionsHMS alumni of the Medical Language Program reported improved language proficiency after the medical language courses' conclusion, suggesting that the courses may catalyze long-term language learning. We found that a majority of respondents reported that the medical language courses influenced their desire to work with individuals who spoke the language of the courses they took. Medical language courses may equip physicians to practice language-concordant care in their careers.

Project description:BackgroundThe growing number of patients visiting pediatric emergency departments could have a detrimental impact on the care provided to children who are triaged as needing urgent attention. Therefore, it has become essential to continuously monitor and analyze the admissions and waiting times of pediatric emergency patients. Despite the significant challenge posed by the shortage of pediatric medical resources in China's health care system, there have been few large-scale studies conducted to analyze visits to the pediatric emergency room.ObjectiveThis study seeks to examine the characteristics and admission patterns of patients in the pediatric emergency department using electronic medical record (EMR) data. Additionally, it aims to develop and assess machine learning models for predicting waiting times for pediatric emergency department visits.MethodsThis retrospective analysis involved patients who were admitted to the emergency department of Children's Hospital Capital Institute of Pediatrics from January 1, 2021, to December 31, 2021. Clinical data from these admissions were extracted from the electronic medical records, encompassing various variables of interest such as patient demographics, clinical diagnoses, and time stamps of clinical visits. These indicators were collected and compared. Furthermore, we developed and evaluated several computational models for predicting waiting times.ResultsIn total, 183,024 eligible admissions from 127,368 pediatric patients were included. During the 12-month study period, pediatric emergency department visits were most frequent among children aged less than 5 years, accounting for 71.26% (130,423/183,024) of the total visits. Additionally, there was a higher proportion of male patients (104,147/183,024, 56.90%) compared with female patients (78,877/183,024, 43.10%). Fever (50,715/183,024, 27.71%), respiratory infection (43,269/183,024, 23.64%), celialgia (9560/183,024, 5.22%), and emesis (6898/183,024, 3.77%) were the leading causes of pediatric emergency room visits. The average daily number of admissions was 501.44, and 18.76% (34,339/183,204) of pediatric emergency department visits resulted in discharge without a prescription or further tests. The median waiting time from registration to seeing a doctor was 27.53 minutes. Prolonged waiting times were observed from April to July, coinciding with an increased number of arrivals, primarily for respiratory diseases. In terms of waiting time prediction, machine learning models, specifically random forest, LightGBM, and XGBoost, outperformed regression methods. On average, these models reduced the root-mean-square error by approximately 17.73% (8.951/50.481) and increased the R2 by approximately 29.33% (0.154/0.525). The SHAP method analysis highlighted that the features "wait.green" and "department" had the most significant influence on waiting times.ConclusionsThis study offers a contemporary exploration of pediatric emergency room visits, revealing significant variations in admission rates across different periods and uncovering certain admission patterns. The machine learning models, particularly ensemble methods, delivered more dependable waiting time predictions. Patient volume awaiting consultation or treatment and the triage status emerged as crucial factors contributing to prolonged waiting times. Therefore, strategies such as patient diversion to alleviate congestion in emergency departments and optimizing triage systems to reduce average waiting times remain effective approaches to enhance the quality of pediatric health care services in China.

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Student characteristics, professional preferences, and admission to medical school.

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Student characteristics, professional preferences, and admission to medical school.

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