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HOW WILL THE QUALITATIVE FEEDBACK FROM SURVEYS FOCUS GROUPS AND INTERVIEWS BE ANALYZED USING NVIVO

NVivo is a qualitative data analysis software developed by QSR International to help users organize, analyze, and find insights in unstructured qualitative data like interviews, focus groups, surveys, articles, social media and web content. Some of the key ways it can help analyze feedback from different qualitative sources are:

Organizing the data: The first step in analyzing qualitative feedback is organizing the different data sources in NVivo. Surveys can be imported directly from tools like SurveyMonkey or Google Forms. Interview/focus group transcriptions, notes and audio recordings can also be imported. This allows collating all the feedback in one place to start coding and analyzing.

Attribute coding: Attributes like participant demographics (age, gender etc.), location, question number can be coded against each respondent to facilitate analysis based on these attributes. This helps subgroup and compare feedback based on attributes when analyzing themes.

Open coding: Open or emergent coding involves reading through the data and assigning codes/labels to text, assigning descriptive names to capture meaning and patterns. This allows identifying preliminary themes and topics emerging from feedback directly from words and phrases used.

Coding queries: As more data is open coded, queries can be run to find all responses related to certain themes, keywords, codes etc. This makes it easy to quickly collate feedback linked to particular topics without manually scrolling through everything. Queries are extremely useful for analysis.

Axial coding: This involves grouping open codes together to form higher level categories and hierarchies. Similar codes referring to same/linked topics are grouped under overarching themes. This brings structure and organization to analysis by grouping related topics together at different abstraction levels.

Case coding: Specific cases or respondents that provide particularly insightful perspective can be marked or coded for closer examination. Case nodes help flag meaningful exemplars in the data for deeper contextual understanding during analysis.

Concept mapping: NVivo allows developing visual concept maps that help see interconnections between emergent themes, sub-themes and categories in a graphical non-linear format. These provide a “big picture” conceptual view of relationships between different aspects under examination.

Coding comparison: Coding comparison helps evaluate consistency of coding between different researchers/coders by comparing amount of agreement. This ensures reliability and rigor in analyzing qualitative data by multiple people.

Coded query reports: Detailed reports can be generated based on different types of queries run. These reports allow closer examination of themes, cross-tabulation between codes/attributes, comparison between cases and sources etc. Reports facilitate analysis of segments from different angles.

Modeling and longitudinal analysis: Relationships between codes and themes emerging over time can be modeled using NVivo. Feedback collected at multiple points can be evaluated longitudinally to understand evolution and changes in perspectives.

With NVivo, all sources – transcripts, notes, surveys, images etc. containing qualitative feedback data are stored, coded and linked to an underlying query-able database structure that allows users to leverage the above and many other tools to thoroughly examine emergent patterns, make connections between concepts and generate insights. The software allows methodically organizing unstructured text based data, systematically coding text segments, visualizing relationships and gleaning deep understanding to inform evidence-based decisions. For any organization collecting rich qualitative inputs regularly from stakeholders, NVivo provides a very powerful centralized platform for systematically analyzing suchfeedback.

NVivo is an invaluable tool for analysts and researchers to rigorously analyze and gain valuable intelligence from large volumes of qualitative data sources like surveys, interviews and focus groups. It facilitates a structured, transparent and query-able approach to coding emergent themes, comparing perspectives, relating concepts and ultimately extracting strategic implications and recommendations backed by evidence from verbatim customer/user voices. The software streamlines what would otherwise be an unwieldy manual process, improving efficiency and credibility of insights drawn.

WHAT WERE THE KEY FINDINGS FROM THE POST FALL HUDDLES AND REVIEWS

Post-fall huddles and reviews are standard care practices implemented by many healthcare organizations to systematically evaluate fall events among patients. The goal of these processes is to identify factors that may have contributed to a fall, mitigate future risks, and prevent repeat falls. After a patient experiences a fall, a multidisciplinary team typically conducts a prompt huddle at the bedside while details are still fresh. They then conduct a more formal review within 1-2 days to analyze findings in depth.

At my facility, we have worked hard over the past year to strengthen our focus on falls prevention as rates had been slowly creeping up. As part of our quality improvement efforts, we began mandating post-fall huddles immediately after any fall and follow-up reviews within 24 hours led by our falls committee. This allowed us to gather a wealth of insightful findings that are helping us better understand falls risks and implement targeted safety interventions.

Some of the most frequently identified contributors to falls uncovered through our huddle and review processes included: a lack of call light usage by patients, gaps in communication of fall risks on shift change handoffs, noncompliance with fall prevention interventions like alarm activation and hip protectors, missed rounds by nursing staff, and an insufficient number of staff to provide needed assistance in a timely manner. Environmental factors like uneven flooring, lack of secure handrails, and poor lighting were also flagged in certain areas as physical plant issues meriting examination.

We also found that patients presenting with certain medical conditions or recently prescribed new medications appear to be at heightened risk and warrant especially close monitoring. Conditions like delirium, confusion, new weakness, and gait instability emerged as common themes among those who sustained injurious falls. New medications that may cause dizziness, drowsiness, or impair balance seemed to interact as risk multipliers as well. Comorbidities like arthritis, impaired vision, and history of prior falls further compounded these risks.

Through analyzing fall circumstances in detail, some falls could likely have been prevented with more astute screening of intrinsic and extrinsic risk factors during admission assessments. Our reviews highlighted opportunities to bolster comprehensive geriatric assessments and apply standardized screening tools to systematically identify individuals’ personal fall histories, mobility limitations, cognitivefunction, vision deficits, and medication regimens that signal increased concern. We also found variable compliance with recommended fall prevention orders across units depending on available staffing resources and competing priorities.

Reviewing nursing documentation provided insights into human factors as well. Some falls occurred when proper assistance was not provided during high-risk activities like toileting/transfers due to staff distractions or simultaneous demands on multiple patients. Communication gaps were also implicated – like when day and night shift nurses failed to exchange all key details about fall risks during handoffs. This points to the need for more reliable standardized communication practices and enhanced teamwork/situational awareness training.

Our falls committee also probed contributing organizational factors. Workload issues, staffing shortages, and high patient volumes contributed to limited time for education, individualized care planning, and consistent implementation of nonpharmacologic fall prevention strategies. Adhering to recommended staffing ratios and skill mixes surfaced as an ongoing challenge. Equipment issues also became evident, such as nonfunctional call lights or beds/chairs lacking appropriate safety features.

This comprehensive evaluation of circumstantial, clinical, human, and system factors through huddles and reviews has generated an invaluable roadmap. We are now better positioned to implement highly targeted multi-pronged interventions shown to make the biggest impact. Actions underway include bolstering admission assessment consistency, improving communication practices, redesigning high-risk spaces, strengthening individualized care planning, enhancing staff education/competencies, and advocatingfor necessary staffing and equipment resources. With continued diligence, I’m hopeful our revised approach will yield safer patient outcomes and lower preventable fall rates over time. The insights gained through post-fall assessment refinement have certainly equipped us to move the needle on this important quality and safety issue.

HOW ARE CAPSTONE PROJECTS AT THE UNIVERSITY OF WATERLOO DIFFERENT FROM REGULAR COURSEWORK

Capstone projects at the University of Waterloo are significantly different from regular coursework that students complete throughout their degree. Capstone projects are intended to serve as a culminating experience that allows students to synthesize and apply knowledge and skills gained throughout their program. They challenge students to take on more open-ended problems without clear solutions and require self-directed work over an extended period of time, usually a term or academic year.

Some key ways that capstone projects differ from regular coursework include:

Scope – Capstone projects are much more open-ended and have a broader scope compared to typical assignments for individual courses. Students are given a general problem or area of focus but have significant freedom to define the specific goals, approach, and deliverables for their project. This requires significantly more self-direction and independent work from students compared to following detailed instructions for assignments.

Time Commitment – Capstone projects are designed to be a major time commitment, often spanning an entire academic term or even a full year for some programs. Students are expected to dedicate hundreds of hours to their capstone compared to the typical few dozen hours spent on individual course assignments. The extended timeframe allows for more in-depth and rigorous work versus short timelines for regular course assignments.

Industry/Community Focus – Many capstone projects directly involve or are focused on an issue or problem from the external community or industry. Students work to address real-world problems and needs versus hypothetical scenarios. This gives capstones an applied, experiential component and makes the work directly relevant to future careers. Close collaboration may occur with external partners, adding another layer of responsibility.

Interdisciplinary Approach – Due to their open-ended nature, capstone projects commonly bring together concepts and skills from across a student’s overall program of study. This encourages an interdisciplinary perspective that is less common in individual discipline-focused courses. Students must integrate diverse areas and consider how different lenses shed light on an issue.

Self-Directed Learning – With more flexibility and less prescribed structure compared to courses, capstone projects require a high degree of self-direction and self-motivation from students. Strong project management, time management, and research skills are crucial as students design their own path and must regularly demonstrate initiative to stay on track and achieve milestones. This replicates real-world expectations.

Oral Presentation – Upon completion, capstone projects usually involve a formal presentation where students must clearly communicate the purpose, processes, outcomes and lessons learned to both experts and non-experts. This helps develop presentation skills that are key for future work and academic opportunities like conferences. Formal written deliverables like reports are also expected.

Evaluation – Assessment of capstone projects emphasizes higher-order competencies like critical thinking, problem-solving, collaboration, and professionalism to a greater degree than most course-level evaluations. Success depends more on how well students independently drive open exploration of a complex challenge versus narrow testing of mastery over prescribed material. Feedback aims to support ongoing professional development.

At the University of Waterloo specifically, capstone experiences occur within co-op work terms for many professional programs like engineering, where students complete major work-related projects. Other programs involve large-scale individual or team research projects completed over an academic term, with faculty advisors acting in a guidance role. Across the board at Waterloo, capstone work epitomizes applying multi-faceted academic training to solve real problems and demonstrate independent project leadership abilities, readying graduates for their future careers.

Capstone projects provide University of Waterloo students with a qualitatively different and more immersive culminating learning experience compared to regular course-based study. By requiring extensive self-directed effort focused on multifaceted real-world issues over an extended timeframe, capstones help ensure Waterloo graduates have intensively developed the wide range of practical and professional competencies that will enable life-long success beyond the academic environment.

WHAT ARE SOME IMPORTANT SKILLS THAT STUDENTS CAN GAIN FROM COMPLETING A MACHINE LEARNING CAPSTONE PROJECT

Students who undertake a machine learning capstone project have the opportunity to gain a wide variety of important technical, professional, and soft skills that will be highly valuable both in their academic and career trajectories. Machine learning is an interdisciplinary field that draws from computer science, statistics, mathematics, and other domains. A capstone project provides students hands-on experience applying machine learning concepts and algorithms to solve real-world problems.

One of the most significant skills students develop is the ability to independently plan and complete an end-to-end machine learning project. This involves skills such as defining objectives, scoping the problem, researching approaches, designing models and experiments, acquiring or collecting data, preparing and cleaning data, implementing and training models, evaluating results, and reporting findings. Learning how to take ownership of a project from start to finish teaches self-direction, time management, and the ability to overcome setbacks independently — skills critical for future academic work as well as most professional careers.

On the technical side, some important skills gained include experience with machine learning algorithms and techniques. Students apply algorithms such as regression, classification, clustering, deep learning, and more to solve practical problems. They gain experience with model building, tuning hyperparameters, debugging models, evaluating accuracy, and comparing approaches. Students also develop software skills like programming in languages like Python, version control with Git, and experiment tracking with platforms like Jupyter Notebooks or MLflow. Foundational skills in data cleaning, exploration, visualization and feature engineering are also greatly improved.

Oral and written communication skills are enhanced through the reporting required to describe their project objectives, methodology, results and conclusions to both technical and non-technical audiences. Students practice disseminating technical ML work clearly and accurately. Presentation experience builds self-assurance and the ability to discuss technical topics with non-experts. Written documents like project reports and blogs improve scientific writing structure and style.

Self-awareness of strengths, weaknesses, and learning style is enhanced through independent work and feedback. Students gain an understanding of their ability to take initiative, manage complexity, tolerate ambiguity, and incorporate feedback to improve. Real-world experience applying academic knowledge raises awareness of how to continuously expand technical competencies.

Teamwork skills may also be developed if the project incorporates a group component. Experience collaborating on shared goals, delegating responsibilities, navigating conflicts, establishing structure and accountability, and combining individual contributions into a cohesive whole strengthens ability to work as part of a team.

Beyond technical prowess, a capstone project showcases many desirable professional qualifications that employers seek, like problem-solving aptitude, work ethic, accountability, versatility and adaptability to new challenges. Completing an independent, multi-stage project provides tangible evidence of persistence, resourcefulness and motivation to see complex, open-ended tasks through to completion—qualities essential for long-term career growth.

The research, experimentation, reporting and reflection involved in a machine learning capstone project provides a true immersion into evidence-based, iterative development practices that closely mimic real-world data science work. The opportunity to gain these wide-ranging practical and professional skills sets students up enormously well for both continued academic success and a rapid start in industry. A well-executed capstone demonstrates to potential employers an applicant’s initiative and capability to contribute immediately as a junior practitioner.

Conducting a machine learning capstone project allows students to gain invaluable experience in key technical skills like machine learning algorithms and software, as well as softer skills in project management, communication, self-awareness and collaboration that will support long-term learning and career development. The hands-on, independent nature of a capstone mimics real working conditions and provides a solid foundation and proof of competency for whatever a student’s next steps may be.

WHAT WERE SOME OF THE KEY INSIGHTS YOU DISCOVERED FROM THE MARKET BASKET ANALYSIS?

Market basket analysis is a data mining technique used to discover associations and correlation relationships between items stored in transactional databases. By analyzing what items are frequently purchased together across many customers, market basket analysis can reveal important purchasing patterns and trends. Some key insights that may be discovered include:

Top Selling Item Combinations: Market basket analysis can identify the most commonly purchased combinations of items. This shows which products are strong complements to each other and are frequently bought together. Knowing the top selling item groupings allows a retailer to better merchandise and display these items near each other in store to drive additional complementary sales. It also enables targeted promotional offers and discounts for the associated products.

Impulse Purchase Relationships: The analysis can uncover items that are often impulse purchases when other items are in the basket. These additive or supplementary items may not have been on a customer’s original shopping list but get added once they see them alongside the planned purchases. Identifying these impulse relationship opens opportunities to actively promote and upsell the accompanying items to increase cart sizes and revenue per transaction.

Substitute or Cannibalization Relationships: The analysis may also find situations where one item is detracting from sales of a similar product. This occurs when customers view two things as substitutes and tend to pick one over the other. Understanding substitution relationships helps a retailer manage product assortments more strategically by potentially removing or replacing items that are cannibalizing each other’s sales.

New Product Introduction Opportunities: By analyzing existing co-purchase patterns, the market basket analysis can identify empty spaces in the data where introducing a new product may spark additional complementary sales. For example, if cookies and milk are regularly bought together, introducing cookie-flavored milk could fill a void and exploit that existing relationship. This helps guide the development and launch of new items tailored to complement current best-sellers.

Preferred Brands and Private Label Opportunities: The analysis provides visibility into which brands customers jointly select and have affinity for. It reveals the brand preferences and loyalties that drive multiple item purchases from the same manufacturer. This information helps retailers optimize brand strategies for their private label offerings, such as developing store brands designed to directly compete with identified co-purchased national brands.

Customer Segment Affinities: The analysis may uncover differences in purchasing patterns between demographic segments. For example, families with children could have distinct item groupings compared to elderly customers. Understanding these nuanced segment associations allows more targeted merchandising, assortments and promotions optimized for each customer type. It also supports the development of customized segment-specific retail experiences both online and in physical stores.

Seasonal and Geographic Tendencies: Market basket findings can expose item combinations that are especially strong during holiday or seasonal time periods. It may also uncover location-based preferences where certain regions show affinity for unique local product blends. This geographic and temporal analyses assist retailers in adjusting their assortments and marketing for optimal relevance based on time of year and community demographics served.

Supply Chain and Inventory Implications: The insights reveal dependencies between items from a demand perspective. This informs procurement, manufacturing, warehousing and store fulfillment by highlighting which products need coordinated replenishment to ensure the right complementary assortments reach shelves together. It supports supply chain optimization to fulfill complete shopping baskets and avoid lost sales from stockouts of key co-purchased items.

Market basket analysis provides a wealth of strategic business intelligence about customer shopping behaviors and the inherent links between products that drive multiple item purchases. The insights gained around top product combinations, impulse relationships, substitutes, brand preferences, seasonal tendencies and more allow retailers to profoundly improve merchandising, assortments, promotions, new product development, operations and overall customer experiences. If leveraged effectively, these findings can significantly boost sales, margins and competitive advantage.