Biostatistics primer: part I

Self-perceived preparedness of new dental graduates from a Middle Eastern university

INTRODUCTION

Preparedness for independent clinical practice is a core goal of undergraduate dental education. The aim of this study was to evaluate the self-reported preparedness for the dental practice among new dental graduates from a University in the Middle East.

METHODS

Following ethics approval, a purposive sampling techniques were used to invite newly qualified dental graduates at a Middle Eastern University. A validated research instrument, namely the Dental Undergraduate Preparedness Assessment Scale (DU-PAS), was used to evaluate the self-perceived preparedness of the participants. Data collection was undertaken online using Google Forms and data were analysed to compute the descriptive statistics, reliability and demographic differences among the participants.

RESULTS

A total of 82 participants provided their response yielding a response rate of 68%. The participants included 49 males and 33 females. The overall reliability of DU-PAS was excellent. (α = 0.93). The participants included less than 40% of participants were able independently to prescribe medications, assess orthodontics needs and perform endodontic treatment on multirooted teeth. In addition, more than 65% indicated low confidence in evaluating new dental materials and products, interpreting new research findings and behavioural management of children.

CONCLUSION

The present findings identified strengths and weaknesses of new dental graduates from a Middle Eastern university. The participants were confident in basic clinical skills but lower confidence was reported for complex dental procedures. The findings highlight the learning needs of new dental graduates and can serve to inform the undergraduate curriculum to enhance the preparedness of future cohorts of dental graduates.

Statistics in clinical research: Important considerations

Statistical analysis is one of the foundations of evidence-based clinical practice, a key in conducting new clinical research and in evaluating and applying prior research. In this paper, we review the choice of statistical procedures, analyses of the associations among variables and techniques used when the clinical processes being examined are still in process. We discuss methods for building predictive models in clinical situations, and ways to assess the stability of these models and other quantitative conclusions. Techniques for comparing independent events are distinguished from those used with events in a causal chain or otherwise linked. Attention then turns to study design, to the determination of the sample size needed to make a given comparison, and to statistically negative studies.

Descriptive statistics: the specification of statistical measures and their presentation in tables and graphs. Part 7 of a series on evaluation of scientific publications

BACKGROUND

Descriptive statistics are an essential part of biometric analysis and a prerequisite for the understanding of further statistical evaluations, including the drawing of inferences. When data are well presented, it is usually obvious whether the author has collected and evaluated them correctly and in keeping with accepted practice in the field.

METHODS

Statistical variables in medicine may be of either the metric (continuous, quantitative) or categorical (nominal, ordinal) type. Easily understandable examples are given. Basic techniques for the statistical description of collected data are presented and illustrated with examples.

RESULTS

The goal of a scientific study must always be clearly defined. The definition of the target value or clinical endpoint determines the level of measurement of the variables in question. Nearly all variables, whatever their level of measurement, can be usefully presented graphically and numerically. The level of measurement determines what types of diagrams and statistical values are appropriate. There are also different ways of presenting combinations of two independent variables graphically and numerically.

CONCLUSIONS

The description of collected data is indispensable. If the data are of good quality, valid and important conclusions can already be drawn when they are properly described. Furthermore, data description provides a basis for inferential statistics.