Academic Integrity and Senior Nursing Undergraduate Clinical Practice

Background: Academic integrity (AI) has been defined as the commitment to the values of honesty, trust, fairness, respect, and responsibility with courage in all academic endeavours. The senior years of nursing studies provide an intersection for students to transition to professional roles through student clinical practice. It is essential to understand what predicts senior nursing students’ intention to behave with AI so that efforts can be directed to initiatives focused on strengthening their commitment to behaving with AI. Research Questions: To what extent do students differ on Theory of Planned Behaviour (TPB) variables? What predicts intention to behave with academic integrity among senior nursing students in clinical practice across three different Canadian Schools of Nursing? Method: The TPB framework, an elicitation (n=30) and two pilot studies (n=59, n=29) resulted in the development of a 38 question (41-item) self-report survey (Miron Academic Integrity Nursing Survey—MAINS: α>0.70) that was administered to Year 3 and 4 students (N=339). Three predictor variables (attitude, subjective norm, perceived behavioural control) were measured with students’ intention to behave with AI in clinical. Age, sex, year of study, program stream, students’ understanding of AI policies, and locations where students accessed AI information were also measured. Results: Hierarchical multiple regression analyses revealed that background, site, and TPB variables explained 32.6% of the variance in intention to behave with academic integrity. The TPB variables explained 26.8% of the variance in intention after controlling for background and site variables. In the final model, only the TPB predictor variables were statistically significant with Attitude having the highest beta value (beta=0.35, p<0.001), followed by Subjective Norm (beta=0.21, p<0.001) and Perceived Behavioural Control (beta=0.12, p<0.02). Conclusion: Student attitude is the strongest predictor to intention to behave with AI in clinical practice and efforts to positively influence students’ attitudes need to be a focus for schools, curricula, and clinical educators. Opportunities for future research should include replicating the current study with students enrolled in other professional programs and intervention studies that examine the effectiveness of specific endeavours to promote AI in practice.

Challenges, Strengths and Opportunities Related to Implementing Comprehensive Evidence-Based Guidelines on Breast Cancer Survivorship Care by Primary Care Physicians and Nurse Practitioners in Southeastern Ontario

Background Many breast cancer survivors continue to have a broad range of physical and psychosocial problems after breast cancer treatment. As cancer centres move forward with earlier discharge of stable breast cancer survivors to primary care follow-up it is important that comprehensive evidence-based breast cancer survivorship care is implemented to effectively address these needs. Research suggests primary care providers are willing to provide breast cancer survivorship care but many lack the knowledge and confidence to provide evidence-based care. Purpose The overall purpose of this thesis was to determine the challenges, strengths and opportunities related to implementing comprehensive evidence-based breast cancer survivorship guidelines by primary care physicians and nurse practitioners in southeastern Ontario. Methods This mixed-methods research was conducted in three phases: (1) synthesis and appraisal of clinical practice guidelines relevant to provision of breast cancer survivorship care within the primary care practice setting; (2) a brief quantitative survey of primary care providers to determine actual practices related to provision of evidence-based breast cancer survivorship care; and (3) individual interviews with primary care providers about the challenges, strengths and opportunities related to provision of comprehensive evidence-based breast cancer survivorship care. Results and Conclusions In the first phase, a comprehensive clinical practice framework was created to guide provision of breast cancer survivorship care and consisted of a one-page checklist outlining breast cancer survivorship issues relevant to primary care, a three-page summary of key recommendations, and a one-page list of guideline sources. The second phase identified several knowledge and practice gaps, and it was determined that guideline implementation rates were higher for recommendations related to prevention and surveillance aspects of survivorship care and lowest related to screening for and management of long-term effects. The third phase identified three major challenges to providing breast cancer survivorship care: inconsistent educational preparation, provider anxieties, and primary care burden; and three major strengths or opportunities to facilitate implementation of survivorship care guidelines: tools and technology, empowering survivors, and optimizing nursing roles. A better understanding of these challenges, strengths and opportunities will inform development of targeted knowledge translation interventions to provide support and education to primary care providers.

The Comparative Effectiveness of EEG Biomarkers in Antidepressant Response and Illness Prediction in Major Depressive Disorder

Background: There is growing evidence that individual EEG differences may aid in classifying patients with major depressive disorder (MDD) and also help predict clinical response to antidepressant treatment. This study aims to compare the effectiveness of EEG frequency band power, alpha asymmetry and prefrontal theta cordance towards escitalopram response prediction and MDD diagnosis, in a multi-site initiative. Methods: Resting EEG (eyes open and closed) was recorded from 64 electrodes in 44 depressed patients and 20 healthy controls at baseline, 2 weeks post-treatment and 8 weeks post-treatment. Clinical response was measured as change from baseline MADRS of 50% or more. EEG measures were analyzed (1) at baseline (2) at 2 weeks post-treatment and (3) as an ‘‘early change” variable defined as change in EEG from baseline to 2 weeks post-treatment. Results: At baseline, responders exhibited greater absolute alpha power in the left hemisphere versus the right while non-responders showed the opposite. Responders further exhibited a cortical asymmetry of greater right relative to left activity in parietal areas. Groups also differed in baseline relative delta power with responders showing greater power in the right hemisphere versus the left while non-responders showed the opposite. At 2 weeks post-treatment, responders exhibited greater absolute beta power in the left hemisphere relative to right and the opposite was noted for non-responders. The opposite pattern was noted for absolute and relative delta power at 2 weeks post-treatment. Responders exhibited early reduction in relative alpha power and early increments in relative theta power. Non-responders showed a significant early increase in prefrontal theta cordance. Absolute delta power helped distinguish MDD patients from healthy controls. Conclusions: Hemispheric asymmetries in the alpha and delta bands at pre-treatment baseline and at 2 weeks post-treatment have moderate to moderately strong predictive utility towards antidepressant treatment response. These findings have significant potential for improving clinical practice in psychiatry by eventually guiding clinical choice of treatments. This would greatly benefit patients awaiting relief from depressive symptoms as treatment optimization would help overcome problems associated with delayed recovery. Our results also indicate that resting EEG activity may have clinical utility in predicting MDD diagnosis.