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Pursuing a targeted dream specialty and a research career: Opinions and observations from a fifth-year medical student’s perspective

Letter to Editor
  • Gehad Mohamed Tawfik,
  • Pages 1-4

With our fast-pacing life, numerous learning and scientific sources and information are available and required for medical students to boost their skills since their early life to accommodate with the great knowledge they take. Medical students should re-elaborate what they studied and exploit knowledge clinically. A good doctor is a good observer, so eyes should be kept on while mentor managing patients in order to add more to our medical notions. A seed to become a great future doctor starts by searching for a specialty that fits your personality, to practice it as a volunteer, to gain its skills earlier. So when you graduate, you have more time to gain other learning experience. As long as you practice it, the more chance to become one of its experts. Managing your patient as a relative, not as a bag of money, is very important to be applied. Inability to diagnose a patient is not a shame, so never let a patient go home without referring him to another doctor who has more experience than you. Having a background in other medical specialties will help you recognize common signs of other related medical conditions that could lead you to refer him to right specialty doctor. Joining a research lab will keep you updated with new inventions, drugs, algorithms, and guidelines, which will help you become more acknowledged with medical problems that you were unaware of. Time management is the key to success as a researcher without affecting your daily life activities and study requirements.

Graphical abstract

Translation and cross-cultural adaptation of the Vietnamese version of the Diabetes Distress Scale

Original Research
  • Ong Phuc Thinh, Huynh Ngoc Van Anh, Do Thanh Tung, To Gia Kien,
  • Pages 5-11

Background: The Diabetes Distress Scale (DDS) is a valid instrument to measure diabetes distress included in American Diabetes Association and Canadian Diabetes Association guidelines but not available in Vietnamese. This study translated and culturally adapted the DDS to assess diabetes distress of Vietnamese type 2 diabetics and evaluated its internal consistency, face and content validity.

Methods: The translation process followed standard guidelines for adaptation of an instrument: forward translation, back translation, synthesis, evaluation by an expert panel and pretest. The expert panel included three English specialists as linguistic experts and six content experts in multidisciplinary areas relevant to the study. The pretest was conducted on a sample of 31 type 2 diabetics in the Endocrinology outpatient clinic at Trung Vuong hospital. Content validity was determined based on experts’ concurrence using content validity index for items (I-CVI). Face validity is assessed by participants in pretest. Internal consistency was measured using Cronbach’s alpha.

Results: Final version was equivalent with the original English version and easy to understand. I-CVI of 17 items were 1.00 in linguistic experts and greater than 0.83 in content experts. All 31 participants involved in the pretest commented that the items were very clear and acceptable regarding their socioeconomic background. Cronbach’s alpha coefficient was 0.76 – 0.93 for each subscale and 0.94 for the overall.

Conclusion: Vietnamese version of the DDS was reliable, face and content-valid to assess diabetes distress in type 2 diabetics among Vietnamese.

Graphical abstract

Directed Acyclic Graphs: Alternative tool for causal inference in epidemiology and biostatistics research and teaching

Review
  • Tran Ngoc Dang, Khuong Quynh Long, Huynh Thi Hong Tram, Le Huynh Thi Cam Hong, Tuan Minh Vo,
  • Pages 12-16
  • 27/10/2018
  • 123
  • 292
  • Free

The issue of causation is one of the major challenges for epidemiologists who aim to understand the association between an exposure and an outcome to explain disease patterns and potentially provide a basis for intervention. Suitably designed experimental studies can offer robust evidence of the causal relationships. The experimental studies, however, are not popular, difficult or even unethical and impossible to conduct; it would be desirable if there is a methodology for reducing bias or strengthening the causal inferences drawn from observational studies. The traditional approach of estimating causal effects in such studies is to adjust for a set of variables judged to be confounders by including them in a multiple regression. However, which variables should be adjusted for as confounders in a regression model has long been a controversial issue in epidemiology. From my observation, the adjustments using only "statistical artifacts" methods such as the p-value<0.2 in univariate analysis, stepwise (forward/backward) are widely used in research and teaching in Epidemiology and Statistics but without appropriated notice on the biological or clinical relationships between exposure and outcome which may induce the bias in estimating causal effects. In this mini-review, we introduce an interesting method, namely Directed Acyclic Graphs (DAGs), which can be used to reduce the bias in estimating causal effects; it is also a good application for Epidemiology and Biostatistics teaching.

Graphical abstract