Essentials Of Evidence-Based Practice Initial Post Introduct

Essentials of Evidence Based Practice INITIAL POST Introduction Elder and Paul

Essentials of Evidence Based Practice INITIAL POST Introduction Elder and Paul defined inferences as “conclusions you come to. It's what the mind does in figuring something out” (2009, as cited by University of Louisville, n.d., para 1). In any research study, conclusions drawn from the results should infer “approximate truth” (Polit and Beck, 2017b, p.217). This is known as validity. The concerns associated with internal validity, the effects of strengthening this type in relation to another in quantitative investigations, and the importance of sound conclusions in research are the topics of this paper.

Article of Focus Yuan, Chou, Hwu, Chang, Hsu, and Kuo (2009), conducted a quasi-experimental study entitled “An Intervention Program to Promote Health-Related Physical Fitness in Nurses”. The researchers’ background work addressed the incidence of musculoskeletal disorders in nurses and physical fitness measures to improve health. The study involved ninety nurses from five different units in a Taiwanese hospital who volunteered as part of the three-month project. Groups were divided into an experimental group, in which members were instructed to use a stair-stepper for 20-30 minutes, at least three times weekly, following which fitness measurements were taken, and a control group, which did not receive the intervention but received fitness measurements prior to the study.

Results showed that the experimental group, with logistic regression used to adjust for confounding variables, had significant improvements in body mass index, grasp strength, flexibility, abdominal muscle durability, and cardiopulmonary function compared to the control group, despite the opposite being true before the study. Internal Validity Concerns, Remedies, and Results According to Polit and Beck (2017b), quasi-experimental studies are very at risk for internal validity threats (p. 226). In this study, selection bias was noted due to lack of randomization to ensure group similarities. All participants were voluntary, which included individuals with different marital statuses, pre-intervention scores, and family commitments. Additionally, all members worked at the same hospital, raising concerns about contamination of results due to interactions or competition among staff (Yuan et al., 2009, p. 1409).

Homogeneity methods, such as grouping individuals according to similarities in confounding variables, could strengthen internal validity but may reduce statistical conclusion validity and external validity. During the study, three nurses quit and one fell ill, leaving 41 control participants, which could bias results and increase differences between groups, thus impacting internal validity. Statistical techniques could address these issues by controlling for attrition effects, yet external validity would be compromised as those who left might differ from those who remained. The study used consistent measurement tools, but it is unclear if the same individuals performed the measurements each time, potentially introducing testing or instrumentation threats.

While the intervention involved only stair-stepper use, improvements in grasp strength and flexibility raise questions about additional activities. The three-month testing interval allowed for temporal clarity, reducing ambiguity regarding cause-effect relationships. Considering intervention complexity and measurement consistency are crucial for internal validity, but attention must be given to external validity limitations owing to sample attrition and clinical setting specificity.

Importance of Validity Consideration in Research Confounding variables distort the understanding of cause-effect relationships between independent and dependent variables, compromising the reliability of conclusions (Polit & Beck, 2017a, p. 723). Researchers must anticipate potential confounders during the planning phase, employing strategies like randomization, stratification, and matching to mitigate their effects. Nevertheless, trade-offs exist; prioritizing internal validity might reduce external validity, limiting generalizability. Researchers must balance these factors based on research goals, resources, and context.

In research, the ultimate goal is to produce credible, replicable evidence that advances understanding. Employing rigorous methods to control confounding variables enhances validity, but over-controlling can diminish the practical applicability of findings. Polit and Beck (2017b) highlight the dilemma in weighing internal validity against external validity, suggesting that researchers should focus on transparency and contextual relevance when interpreting results. Recognizing that research evidence supports hypotheses rather than proves them underscores the importance of cautious interpretation and ongoing inquiry.

Paper For Above instruction

In the realm of research methodology, understanding and ensuring validity is fundamental to generating trustworthy evidence. Internal validity pertains to the degree to which the observed effects in a study are attributable to the intervention itself rather than extraneous factors, while external validity concerns the generalizability of findings beyond the specific study setting. As demonstrated in Yuan et al.'s (2009) quasi-experimental investigation into physical fitness programs for nurses, numerous threats to internal validity can arise, demanding careful methodological considerations to uphold research integrity.

Yuan et al.'s study aimed to evaluate the impact of stair-stepper exercises on various health metrics among nurses, a population vulnerable to musculoskeletal issues. The quasi-experimental design, although practical in real-world healthcare settings, inherently carries susceptibility to internal validity threats such as selection bias. Since participants self-selected into the study, and randomization was absent, the initial equivalence of groups could be compromised. For instance, differences in pre-existing health conditions, motivation levels, or work schedules might influence outcomes independently of the intervention. Recognizing this, the researchers employed logistic regression to adjust for confounders, yet some residual bias might persist.

Additional threats to internal validity include attrition, as observed when three nurses exited the study and one fell ill, reducing the sample size and potentially skewing the results. Attrition threatens internal validity by introducing systematic differences between those who complete the study and those who drop out. To mitigate this, statistical techniques such as intention-to-treat analysis or multiple imputation could be employed, aiming to preserve the initial group balance and enhance the robustness of conclusions. Moreover, the homogeneity of the sample—being limited to nurses within a single hospital—further constrains internal validity, as the findings may not translate to different populations or settings.

Contamination between groups is another internal validity concern. Since all participants worked within the same hospital, interactions could lead to control group members adopting behaviors from the experimental group, diluting the observed effects. Strategies like cluster randomization or physical separation of groups can address such threats but might not always be feasible in logistical contexts.

The researchers also relied on consistent measurement tools for fitness assessments, which enhances internal validity by reducing instrumentation threats. However, the article does not specify whether the same individuals performed measurements at different time points, an aspect that could influence test-retest reliability. Ensuring measurement consistency is vital for accurate assessment of intervention effects.

Beyond internal validity, external validity—the extent to which findings can be generalized—must be considered. The specific demographic and occupational context of Taiwanese hospital nurses limits the generalizability to other healthcare systems, professions, or cultural backgrounds. To enhance external validity, future research might involve multi-site studies with diverse participant profiles.

Methodologically, trade-offs are inevitable. Strengthening internal validity through randomization, control of confounders, and maintaining measurement consistency may reduce external validity by narrowing the participant scope or setting. Conversely, broad inclusion criteria can improve generalizability but potentially introduce confounding factors that threaten internal validity.

In conclusion, Yuan et al.’s study exemplifies the practical challenges in balancing internal and external validity in healthcare research. Researchers must carefully design studies to control bias and confounding variables while remaining mindful of the real-world applicability of findings. Critical appraisal of internal validity threats and thoughtful implementation of remedies ensure that research conclusions are credible and useful for advancing nursing practice. Ultimately, researchers should recognize the limitations inherent in their designs and interpret results within appropriate contexts, supporting evidence-based decision-making that benefits patient care and health outcomes.

References

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  • Polit, D. F., & Beck, C. T. (2017b). Rigor and validity in quantitative research. In Polit, D. F., & Beck, C. T. Nursing research: Generating and assessing evidence for nursing practice (10th ed., pp. 725-738). Wolters Kluwer.
  • Yuan, S., Chou, M., Hwu, L., Chang, Y., Hsu, W., & Kuo, H. (2009). An intervention program to promote health-related physical fitness in nurses. Journal of Clinical Nursing, 18(10), 1404-1411. https://doi.org/10.1111/j.1365-2702.2008.02677.x
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