Critique of Correlates and Predictors of Missed Nursing Care in Hospitals

Running head: CRITIQUE
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1CRITIQUE
Introduction- Missed care is commonly defined as care that is unfinished, implicitly
rationed or left undone and represents a domain of health and social care that is underused
and creates significant negative impacts on the health and safety of patients (Jones, Hamilton
& Murry, 2015). One of the most crucial skills that are imperative for new nursing
professionals involve the capability to prioritize patient care at the time of admissions. It has
been found that variations in patient condition, medications or supplies, unobtainable
equipment, demand from physicians, and other unforeseeable circumstances often derail the
plan of care delivery (Blackman et al., 2015). This essay will contain a critical appraisal of
the article- Bragadóttir, H., Kalisch, B. J., & Tryggvadóttir, G. B. (2017). Correlates and
predictors of missed nursing care in hospitals. Journal of clinical nursing, 26(11-12), 1524-
1534. In order to address the consequences of missed nursing care (MNC) the researchers
tried to evaluate the correlates and predictors for the same.
Research problem and its significance- The researcher addressed a well-focused issue
based on the fact that errors in hospitals due to missed nursing care (MNC) has been
identified as a potential factor that leads to patient death (Bragadóttir, Kalisch &
Tryggvadóttir, 2017). Most patient safety initiatives place an emphasis on lowering the risks
of fatal errors that occur due to missed care. The research was based on the fact that
notwithstanding the nation and setting, some of the most commonly identified aspects of
MNC encompass mouth care, on time feeding patients, turning and ambulating patients,
comfort talk, patient education, documentation, medication administration, and
interdisciplinary rounds (Kalisch & Xie, 2014). In addition, the authors were also correct in
recognising the fact that magnet hospitals demonstrate a significant reduction in the overall
MNC rates, when compared to non-magnet counterparts, and some of the common reasons
that contribute to the situation include communication and labour resources (Friese et al.,
2015). In addition, the researchers were also correct in identifying poor work environment as

2CRITIQUE
a major predictor of MNC, which in turn resulted in a deterioration of health outcomes and
increased the rates of hospital readmissions (Bragadóttir, Kalisch & Tryggvadóttir, 2017).
Significance of the research problem can be accredited to the fact that missed care reduces
patient satisfaction. Hence, identification of the correlates and predictors would help
healthcare professionals take necessary efforts to enhance patient health and wellbeing
(Kalisch, Xie & Dabney, 2014). Owing to the fact that missed care forms a critical
component of medical error, the prominence of the research problem is that the findings will
help in making necessary changes in the healthcare policies of organisations, with the aim of
reducing the prevalence of MNC.
Research design and methods- The researchers adopted a cross-sectional design for
evaluating the predictors and correlates of MNC in hospitals. Adoption of this research
design is advantageous owing to the fact that it helps in proving assumptions, captures data
within a particular time frame, and also contains provides a snapshot of several variables
within the research duration. In addition, usage of data that has been routinely collected
facilitates the condition of large investigation at little or no expenditure. However, potential
limitations of this research design include the failure of this approach in determine causal
effect, and limitations in analysing particular behaviour over a definite time (Sedgwick,
2014). The researchers recruited 865 nursing professionals who were involved in providing
direct care to the patients, through the process of purposive sampling (Bragadóttir, Kalisch &
Tryggvadóttir, 2017). Hence, the sampling process was advantageous since selection of
participants was done on the basis of attributes that are relevant within the target population
group, which in turn facilitated generalisation of the collected data (Etikan, Musa &
Alkassim, 2016). Nonetheless, the process has been found to be enormously susceptible to
researcher bias.