A Digital Mask-Voiceprint System for Postpandemic Surveillance and Tracing Based on the STRONG Strategy

Lockdowns and border closures due to COVID-19 imposed mental, social, and financial hardships in many societies. Living with the virus and resuming normal life are increasingly being advocated due to decreasing virus severity and widespread vaccine coverage. However, current trends indicate a continued absence of effective contingency plans to stop the next more virulent variant of the pandemic. The COVID-19-related mask waste crisis has also caused serious environmental problems and virus spreads. It is timely and important to consider how to precisely implement surveillance for the dynamic clearance of COVID-19 and how to efficiently manage discarded masks to minimize disease transmission and environmental hazards. In this viewpoint, we sought to address this issue by proposing an appropriate strategy for intelligent surveillance of infected cases and centralized management of mask waste. Such an intelligent strategy against COVID-19, consisting of wearable mask sample collectors (masklect) and voiceprints and based on the STRONG (Spatiotemporal Reporting Over Network and GPS) strategy, could enable the resumption of social activities and economic recovery and ensure a safe public health environment sustainably.

How to provide tailored career coaching for medical students

PURPOSE

This study was performed to develop a counseling strategy, based on the profiles of medical students' Strong Interest Inventory (STRONG) and Myer-Briggs Type Indicator (MBTI) results, focusing on the three following questions: Into what distinct levels are students categorized by STRONG and MBTI? and What is the dispersion of the integrated profiles?

METHODS

Freshmen students from Konyang University College of Medicine who matriculated between March 2011 and 2013 were administered the MBTI personality type test and the STRONG interest inventory assessment. The integrated profiles were categorized per Kim et al. (2006), and frequency analysis was performed with the collected data, using SPSS version 21.0.

RESULTS

Regarding MBTI types, 16.9% of students were categorized as ESTJ, and 12.9% was ISTJ. Further, 62.4% of students were Investigative (I) according to STRONG. The integrated profiles were divided into four types, according to their unclear/clear preference in the STRONG and MBTI results. Most students had 'clear preference and clear interest' (n=144, 80.9%), six students (3.4%) had 'clear interest but unclear preference,' and 28 students (15.7%) showed 'unclear interest but clear preference.'

CONCLUSION

Using the combined results of the STRONG interest inventory assessment and MBTI tools, we can purvey more tailored information to students.