Data Analysis in Nursing Research

Introduction

Research study on improving the safety of caregivers when they are turning and repositioning patients was formulated with objective of establishing how new the method of turning and repositioning patients reduced the occupational musculoskeletal risks faced by the caregivers and patient outcome. This paper outlines the element of data collection as applied in the methodology section of the study.

Background: Data Collection

Health care is faced with increasingly diverse data collection and reporting requirements from a broad scope. Currently, the extensive array of data collection methods includes many variations in measurement across the various qualities of performance and reporting in health care systems have resulted in duplications of the work done, increased expense and performance depletion (Brahami et al., 2013).

In the research done by Fragala et al., (2014), a total of 24 trials were performed on turning and repositioning of patients, though one trial was not recorded. Twelve caregivers volunteered to participate in the study, performing the task twice and then recording the data in Borg scale. The method involved two parts, one repositioning using the usual conventional method and then applying new turning and repositioning system (TAP). Also notably, the participants were instructed prior the research to understand and familiarize with the procedure of turning and repositioning the patients on their beds.

The sample size in a research study is critical. Most importantly, the large sample is more representative of the population, therefore, limiting the outliers or final data (Nicolaou &Masoner, 2013). Furthermore, a broad cross-section provides necessary result for different population spectrum, while broadening the range of possible data displaying a clear perspective of the study. However, in the study by Fragala et al., (2014), a limited number of the trial were conducted with no controlling factors such as variation in body weight and resulting possible distortion of the result and biased conclusion. Erika et al., (2013) noted that a sampling size has a significant influence on the outcome of the survey, for it to be conclusive; the optimal number of the participant should be used.

The study employed Borg scale to record the participants’ exertion immediately after the experiment. Borg Rating of Perceived Exertion (RPE) is a form of measuring intensity levels of physical activity, such as activities of muscular organs. Although, research have validated Borg Scale for Perceived Exertion to being effective, therefore widely used in ergonomics and muscular loading (Jakobsen et al., 2014). A study by Muyor (2013), found the OMI cycle pictorial to be more reliable and valid than Borg scale during a treadmill exercise involving young girls with 86-89% reliability in comparison to 66-70% for Borg scale.

Conclusion

The data collected in a study is a crucial factor to the research. For accuracy of the research, an efficient method of data collection and recording is of equal importance to the data collected. Furthermore, the scope of the participant, the engagement, and understanding of the study determines the validity of the research.

References

Brahami, M., Atmani, B., & Matta, N. (2013). Dynamic knowledge mapping guided by data mining: Application on Healthcare. Journal Of Information Processing Systems, 9(1), 1-30.

Fragala, G. & Fragala, M. (2014). Improving the Safety of Patient Turning and Repositioning Tasks for Caregivers. Workplace Health & Safety, 62(7), 268-273.

Jakobsen, M., Sundstrup, E., Persson, R., Andersen, C., & Andersen, L. (2013). Is Borg’s perceived exertion scale a useful indicator of muscular and cardiovascular load in blue-collar workers with lifting tasks? A cross-sectional workplace study. European Journal Of Applied Physiology, 114(2), 425-434.

Muyor, J. (2013). Exercise Intensity and Validity of the Ratings of Perceived Exertion (Borg and OMNI Scales) in an Indoor Cycling Session. Journal Of Human Kinetics, 39(1).

Nicolaou, A. & Masoner, M. (2013). Sample size requirements in structural equation models under standard conditions. International Journal Of Accounting Information Systems, 14(4), 256-274.

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