Factors associated with mental health of medical students in Vietnam: A national study

This project was the first national study of the health and wellbeing of medical students in Vietnam. Data from over 2,000 students from eight universities indicate that, while the majority are healthy, significant proportions have poor mental and/or physical health and other life adversities. For many students, heavy academic demands were not a major stressor; rather, difficulties within their family, interpersonal relations, dissatisfaction with career choice and housing and financial problems appear to cause the most strain. This study provides evidence that will be useful for the development of professional counseling services in Vietnamese universities.

Using decision trees to understand structure in missing data

Objectives Demonstrate the application of decision trees – classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs) – to understand structure in missing data. Setting Data taken from employees at three different industry sites in Australia. Participants 7915 observations were included. Materials and Methods The approach was evaluated using an occupational health dataset comprising results of questionnaires, medical tests, and environmental monitoring. Statistical methods included standard statistical tests and the ‘rpart’ and ‘gbm’ packages for CART and BRT analyses, respectively, from the statistical software ‘R’. A simulation study was conducted to explore the capability of decision tree models in describing data with missingness artificially introduced. Results CART and BRT models were effective in highlighting a missingness structure in the data, related to the Type of data (medical or environmental), the site in which it was collected, the number of visits and the presence of extreme values. The simulation study revealed that CART models were able to identify variables and values responsible for inducing missingness. There was greater variation in variable importance for unstructured compared to structured missingness. Discussion Both CART and BRT models were effective in describing structural missingness in data. CART models may be preferred over BRT models for exploratory analysis of missing data, and selecting variables important for predicting missingness. BRT models can show how values of other variables influence missingness, which may prove useful for researchers. Conclusion Researchers are encouraged to use CART and BRT models to explore and understand missing data.

Active learning: A step towards automating medical concept extraction

Objective This paper presents an automatic active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort, and (2) the robustness of incremental active learning framework across different selection criteria and datasets is determined. Materials and methods The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional Random Fields as the supervised method, and least confidence and information density as two selection criteria for active learning framework were used. The effect of incremental learning vs. standard learning on the robustness of the models within the active learning framework with different selection criteria was also investigated. Two clinical datasets were used for evaluation: the i2b2/VA 2010 NLP challenge and the ShARe/CLEF 2013 eHealth Evaluation Lab. Results The annotation effort saved by active learning to achieve the same effectiveness as supervised learning is up to 77%, 57%, and 46% of the total number of sequences, tokens, and concepts, respectively. Compared to the Random sampling baseline, the saving is at least doubled. Discussion Incremental active learning guarantees robustness across all selection criteria and datasets. The reduction of annotation effort is always above random sampling and longest sequence baselines. Conclusion Incremental active learning is a promising approach for building effective and robust medical concept extraction models, while significantly reducing the burden of manual annotation.

The impact of a self-development coaching programme on medical and dental students’ psychological health and academic performance: A randomised controlled trial

Background Psychological distress is well-documented worldwide among medical and dental students. Few studies have assessed the impact of self-development coaching programs on the students’ psychological health. The aim of the study was to evaluate the effect of a self-development coaching programme on the psychological health and academic performance of preclinical medical and dental students at Umm Al-Qura University, Saudi Arabia. Methods Four-hundred and twenty-two participants (n = 422, 20–22 years) fulfilled the study requirements and were invited into a parallel-randomised controlled trial that was partially blinded. Participants were stratified by faculty, gender, and academic year, and then randomised. A total of 156 students participated in the intervention group (IG) and 163 students participated in the control group (CG). The IG received the selfdevelopment programme, involving skills and strategies aimed to improve students’ psychological health and academic performance, through a two-day workshop. Meanwhile, the CG attended an active placebo programme focussing on theoretical information that was delivered through a five-hour workshop. Both programmes were conducted by the same presenter during Week 1 of the second semester of the 2012–2013 academic year. Data were gathered immediately before (T1), one week after (T2) and five weeks (T3) after the intervention. Psychological health was measured using the Depression Anxiety Stress Scale (DASS-21), the General Self-Efficacy (GSE), and the Satisfaction With Life Scale (SWLS). Academic performance was measured using students’ academic weighted grades (WG). Student cognitive and emotional perceptions of the intervention were measured using the Credibility/Expectancy Questionnaire (CEQ). Results Data from 317 students, who completed the follow ups, were analysed across the three time periods (IG, n = 155; CG, n = 162). The baseline variables and demographic data of the IG and CG were not significantly different. The IG showed short-term significant reductions in depression and anxiety in compared to CG from T1 to T2. The short-term changes in stress, GSE and SWLS of the IG were not significantly different from those of the CG. While both groups showed a significant change on most of the psychological variables from T1 to T3, no significant differences were found between the groups in this period. In addition, no significant difference was found in WG between the IG and CG after the intervention. No harms relevant to the intervention were reported. Conclusion The investigated self-development coaching programme showed only a short-term improvement on depression and anxiety compared with an active control. There was no effect of the intervention on academic performance.

Occupational injury risk among Australian paramedics: An analysis of national data

Objective To identify the occupational risks for Australian paramedics, by describing the rate of injuries and fatalities and comparing those rates with other reports. Design and participants Retrospective descriptive study using data provided by Safe Work Australia for the period 2000–2010. The subjects were paramedics who had been injured in the course of their duties and for whom a claim had been made for workers compensation payments. Main outcome measures Rates of injury calculated from the data provided. Results The risk of serious injury among Australian paramedics was found to be more than seven times higher than the Australian national average. The fatality rate for paramedics was about six times higher than the national average. On average, every 2 years during the study period, one paramedic died and 30 were seriously injured in vehicle crashes. Ten Australian paramedics were seriously injured each year as a result of an assault. The injury rate for paramedics was more than two times higher than the rate for police officers. Conclusions The high rate of occupational injuries and fatalities among paramedics is a serious public health issue. The risk of injury in Australia is similar to that in the United States. While it may be anticipated that injury rates would be higher as a result of the nature of the work and environment of paramedics, further research is necessary to identify and validate the strategies required to minimise the rates of occupational injury for paramedics.

Determinants of the citation rate of medical research publications from a developing country

Background The number of citations received by an article is considered as an objective marker judging the importance and the quality of the research work. The present study aims to study the determinants of citations for research articles published by Sri Lankan authors. Methods Papers were selectively retrieved from the SciVerse Scopus® (Elsevier Properties S.A, USA) database for 10 years from 1st January 1997 to 31st December 2006, of which 50% were selected for inclusion by simple random sampling. The primary outcome measure was citation rate (defined as the number of citations during the 2 subsequent years after publication). Citation data was collected using the SciVerse Scopus® Citation Analyzer and self citations were excluded. A linear regression analysis was performed with ‘number of citations’ as the continuous dependent variable and other independent variables. Result The number of publications has steadily increased during the period of study. Over three quarter of papers were published in international journals. More than half of publications were research studies (55.3%), and most of the research studies were descriptive cross-sectional studies (27.1%). The mean number of citations within 2 years of publication was 1.7 and 52.1% of papers were not cited within the first two years of publication. The mean number of citations for collaborative studies (2.74) was significantly higher than that of non-collaborative studies (0.66). The mean number of citations did not significantly change depending on whether the publication had a positive result (2.08) or not (2.92) and was also not influenced by the presence (2.30) or absence (1.99) of the main study conclusion in the title of the article. In the linear regression model, the journal rank, number of authors, conducting the study abroad, being a research study or systematic review/meta-analysis and having regional and/or international collaboration all significantly increased the number of citations. Conclusion The journal rank, number of authors, conducting the study abroad, being a research study or systematic review/meta-analysis and having regional and/or international collaboration all significantly increased the number of citations. However, the presence of a positive result in the study did not influence the citation rate.

Constructing a traditional Chinese medicine data warehouse application

The explosive growth in the development of Traditional Chinese Medicine (TCM) has resulted in the continued increase in clinical and research data. The lack of standardised terminology, flaws in data quality planning and management of TCM informatics are preventing clinical decision-making, drug discovery and education. This paper argues that the introduction of data warehousing technologies to enhance the effectiveness and durability in TCM is paramount. To showcase the role of data warehousing in the improvement of TCM, this paper presents a practical model for data warehousing with detailed explanation, which is based on the structured electronic records, for TCM clinical researches and medical knowledge discovery.

Cardiovascular diseases and vulnerable plaques: data, modeling, predictions and clinical applications PREFACE

Introduction Two symposia on “cardiovascular diseases and vulnerable plaques” Cardiovascular disease (CVD) is the leading cause of death worldwide. Huge effort has been made in many disciplines including medical imaging, computational modeling, bio- mechanics, bioengineering, medical devices, animal and clinical studies, population studies as well as genomic, molecular, cellular and organ-level studies seeking improved methods for early detection, diagnosis, prevention and treatment of these diseases [1-14]. However, the mechanisms governing the initiation, progression and the occurrence of final acute clinical CVD events are still poorly understood. A large number of victims of these dis- eases who are apparently healthy die suddenly without prior symptoms. Available screening and diagnostic methods are insufficient to identify the victims before the event occurs [8,9]. Most cardiovascular diseases are associated with vulnerable plaques. A grand challenge here is to develop new imaging techniques, predictive methods and patient screening tools to identify vulnerable plaques and patients who are more vulnerable to plaque rupture and associated clinical events such as stroke and heart attack, and recommend proper treatment plans to prevent those clinical events from happening. Articles in this special issue came from two symposia held recently focusing on “Cardio-vascular Diseases and Vulnerable Plaques: Data, Modeling, Predictions and Clinical Applications.” One was held at Worcester Polytechnic Institute (WPI), Worcester, MA, USA, July 13-14, 2014, right after the 7th World Congress of Biomechanics. This symposium was endorsed by the World Council of Biomechanics, and partially supported by a grant from NIH-National Institute of Biomedical Image and Bioengineering. The other was held at Southeast University (SEU), Nanjing, China, April 18-20, 2014.

ADAMIS: a database for medical information systems

This paper describes the design and implementation of ADAMIS (‘A database for medical information systems’). ADAMIS is a relational database management system for a general hospital environment. Apart from the usual database (DB) facilities of data definition and data manipulation, ADAMIS supports a query language called the ‘simplified medical query language’ (SMQL) which is completely end-user oriented and highly non-procedural. Other features of ADAMIS include provision of facilities for statistics collection and report generation. ADAMIS also provides adequate security and integrity features and has been designed mainly for use on interactive terminals.

Estimating equations with nonignorably missing response data

Troxel, Lipsitz, and Brennan (1997, Biometrics 53, 857-869) considered parameter estimation from survey data with nonignorable nonresponse and proposed weighted estimating equations to remove the biases in the complete-case analysis that ignores missing observations. This paper suggests two alternative modifications for unbiased estimation of regression parameters when a binary outcome is potentially observed at successive time points. The weighting approach of Robins, Rotnitzky, and Zhao (1995, Journal of the American Statistical Association 90, 106-121) is also modified to obtain unbiased estimating functions. The suggested estimating functions are unbiased only when the missingness probability is correctly specified, and misspecification of the missingness model will result in biases in the estimates. Simulation studies are carried out to assess the performance of different methods when the covariate is binary or normal. For the simulation models used, the relative efficiency of the two new methods to the weighting methods is about 3.0 for the slope parameter and about 2.0 for the intercept parameter when the covariate is continuous and the missingness probability is correctly specified. All methods produce substantial biases in the estimates when the missingness model is misspecified or underspecified. Analysis of data from a medical survey illustrates the use and possible differences of these estimating functions.

Multidisciplinary review of teaching and assessing competency attainment by non-medical prescribers

Background Prescribing is a complex task, requiring specific knowledge and skills, and the execution of effective, context-specific clinical reasoning. Systematic reviews indicate medical prescribing errors have a median rate of 7% [IQR 2%-14%] of medication orders [1-3]. For podiatrists pursuing prescribing rights, a clear need exists to ensure practitioners develop a well-defined set of prescribing skills, which will contribute to competent, safe and appropriate practice. Aim To investigate the methods employed to teach and assess the principles of effective prescribing in the undergraduate podiatry program and compare and contrast these findings with four other non-medical professions who undertake prescribing after training at Queensland University of Technology. Method The NPS National Prescribing Competency Standards were employed as the prescribing standard. A curriculum mapping exercise was undertaken to determine whether the prescribing principles articulated in the competency standards were addressed by each profession. Results A range of methods are currently utilised to teach prescribing across disciplines. Application of prescribing competencies to the context of each profession appears to influence the teaching methods used. Most competencies were taught using a multimodal format, including interactive lectures, self-directed learning, tutorial sessions and clinical placement. In particular clinical training was identified as the most consistent form of educating safe prescribers across all five disciplines. Assessment of prescribing competency utilised multiple techniques including written and oral examinations and research tasks, case studies, objective structured clinical examination exercises and the assessment of clinical practice. Effective and reliable assessment of prescribing undertaken by students in diverse settings remains challenging e.g. that occurring in the clinical practice environment. Conclusion Recommendations were made to refine curricula and to promote efficient cross-discipline teaching by staff from the disciplines of podiatry, pharmacy, nurse practitioner, optometry and paramedic science. Students now experience a sophisticated level of multidisciplinary learning in the clinical setting which integrates the expertise and skills of experience prescribers combined with innovative information technology platforms (CCTV and live patient assessments). Further work is required to establish a practical, effective approach to the assessment of prescribing competence especially between the university and clinical settings.

Great Barrier Reef Fish Biological Data

Biological measurements on fish sampled during the course of FRDC funded project Growth, Reproduction and Recruitment of Great Barrier Reef Food Fish Stocks (FRDC 90/18). The comma-delimited ascii file comprises the following fields: 1. Cruise number 2. Date (d-m-y) 3, Region (descriptor of part of Queensland coast or Great Barrier Reef system) 4. Reef (name or number) 5. Data source (Res=research, Rec=recreational fisher, Com=commercial fisher) 6. Capture method 7. Trap number (where appropriate) 8. Species name 9. LthStd (standard length, cm) 10. LthFrk (fork length, cm) 11. LthTot (total length, cm) 12. WtTot (approx total weight, g; weighed at sea) 13. FrameWt (weight of frame [after filleting, with viscera], g; weighed in lab) 14. Sex (macroscopic examination only) 15. GonadWt (g) Data obtained by the Department Employment, Economic Development and Innovation (formerly Primary Industries and Fisheries) between 1988 and 1993, primarily in the southern Great Barrier Reef (Capricorn-Bunker and Swain Groups), with fish traps and handlining.

Statistical analysis of spectral data: A methodology for designing an intelligent monitoring system for the diabetic foot

Early detection of (pre-)signs of ulceration on a diabetic foot is valuable for clinical practice. Hyperspectral imaging is a promising technique for detection and classification of such (pre-)signs. However, the number of the spectral bands should be limited to avoid overfitting, which is critical for pixel classification with hyperspectral image data. The goal was to design a detector/classifier based on spectral imaging (SI) with a small number of optical bandpass filters. The performance and stability of the design were also investigated. The selection of the bandpass filters boils down to a feature selection problem. A dataset was built, containing reflectance spectra of 227 skin spots from 64 patients, measured with a spectrometer. Each skin spot was annotated manually by clinicians as "healthy" or a specific (pre-)sign of ulceration. Statistical analysis on the data set showed the number of required filters is between 3 and 7, depending on additional constraints on the filter set. The stability analysis revealed that shot noise was the most critical factor affecting the classification performance. It indicated that this impact could be avoided in future SI systems with a camera sensor whose saturation level is higher than 106, or by postimage processing.

Pseudo-time marching schemes for inverse problems in structural health assessment and medical imaging

We propose a self-regularized pseudo-time marching strategy for ill-posed, nonlinear inverse problems involving recovery of system parameters given partial and noisy measurements of system response. While various regularized Newton methods are popularly employed to solve these problems, resulting solutions are known to sensitively depend upon the noise intensity in the data and on regularization parameters, an optimal choice for which remains a tricky issue. Through limited numerical experiments on a couple of parameter re-construction problems, one involving the identification of a truss bridge and the other related to imaging soft-tissue organs for early detection of cancer, we demonstrate the superior features of the pseudo-time marching schemes.

Blended learning in medical imaging pre-clinical skills training – does it work?

Undergraduate Medical Imaging (MI)students at QUT attend their first clinical placement towards the end of semester two. Students undertake two (pre)clinical skills development units – one theory and one practical. Students gain good contextual and theoretical knowledge during these units via a blended learning model with multiple learning methods employed. Students attend theory lectures, practical sessions, tutorial sessions in both a simulated and virtual environment and also attend pre-clinical scenario based tutorial sessions. The aim of this project is to evaluate the use of blended learning in the context of 1st year Medical Imaging Radiographic Technique and its effectiveness in preparing students for their first clinical experience. It is hoped that the multiple teaching methods employed within the pre-clinical training unit at QUT builds students clinical skills prior to the real situation. A quantitative approach will be taken, evaluating via pre and post clinical placement surveys. This data will be correlated with data gained in the previous year on the effectiveness of this training approach prior to clinical placement. In 2014 59 students were surveyed prior to their clinical placement demonstrated positive benefits of using a variety of learning tools to enhance their learning. 98.31%(n=58)of students agreed or strongly agreed that the theory lectures were a useful tool to enhance their learning. This was followed closely by 97% (n=57) of the students realising the value of performing role-play simulation prior to clinical placement. Tutorial engagement was considered useful for 93.22% (n=55) whilst 88.14% (n=52) reasoned that the x-raying of phantoms in the simulated radiographic laboratory was beneficial. Self-directed learning yielded 86.44% (n=51). The virtual reality simulation software was valuable for 72.41% (n=42) of the students. Of the 4 students that disagreed or strongly disagreed with the usefulness of any tool they strongly agreed to the usefulness of a minimum of one other learning tool. The impact of the blended learning model to meet diverse student needs continues to be positive with students engaging in most offerings. Students largely prefer pre -clinical scenario based practical and tutorial sessions where 'real-world’ situations are discussed.