Needs Assessment

Aim of Assessment

The needs assessment project utilized a strength-based approach, emphasizing and building on the organization's existing successes. This method was crucial in enhancing the already strong safety culture and ensuring its continuation as a key part of our operations. It also focused on effectively transferring knowledge and skills to students. By concentrating on our strengths, we were able to foster a learning environment where students developed confidence and competence. This approach not only maintained high safety standards but also facilitated continuous improvement and learning, leveraging the organization's collective expertise and experience.

Appreciative Inquiry

In analyzing MIG University's Advanced Materials Lab, we employed a strengths-based approach to discern and define the lab's operational status. We started by creating key questions to reveal the lab's key competencies and success-driven practices. Our objective was to understand what elements contribute to AML's effectiveness and distinctiveness. We aimed not just to recognize these practices but also to contemplate their adoption in other contexts for comparable success. This led us to envisage an ideal state for the lab, capitalizing on its current strengths while pinpointing areas for potential growth and improvement.

Organization

college-campus-student-walking-front-generic-grey-stone-building-146875084.jpg

The Advanced Materials Laboratory at MIG University is a renowned institution specializing in the study of material processing and behavior under extreme conditions. The lab garners support from a diverse range of sponsors, including prestigious entities like the National Science Foundation, the Department of Energy, the Department of Defense, and the Idaho National Laboratory. Reflecting its commitment to excellence and safety, the laboratory has been consecutively recognized as the safest research facility for five years at MIG University. At its core, the laboratory thrives on the dynamic synergy of its team, typically composed of 3-7 undergraduate students alongside an equal number of graduate students. This blend of enthusiasm and advanced academic expertise fosters an environment ripe for innovation and groundbreaking research.

Process

Data Collection

Semi-Formal
Interviews

Extant Data Review

Our primary method of data collection involved conducting semi-formal qualitative interviews, supported by an analysis of incident records. The interviews were designed to explore each of the six dimensions of the BEM, ensuring a comprehensive exploration of key aspects of the lab's environment.

Additionally, we utilized existing data, primarily to confirm or disconfirm the findings revealed in the interviews. This data, drawn from a variety of sources including past reports, performance metrics, and research outcomes, provided a comprehensive backdrop to our analysis. By cross-referencing interview insights with this empirical data, we were able to validate the accuracy of our observations and ensure a well-rounded understanding of the lab's operations.

Data Analysis

Chevalier’s Updated Behavior Engineering Model (BEM)

Coding Qualitative Data - Codebook Creation

Adapted from Chevalier (2003)

We chose Chevalier's Behavior Engineering Model (BEM) for data analysis because of its ability to dissect and understand behaviors and competencies within the lab. BEM's structured framework allowed for effective categorization of data, highlighting key behaviors and patterns. This was vital in identifying practices contributing to the lab's success. BEM also provided a clear structure for merging qualitative and quantitative data, ensuring a comprehensive and integrated analysis.

In our interview process, we implemented a meticulous coding system to analyze the responses effectively. This coding process involved categorizing interview data into distinct themes and behaviors, which were then systematically aligned with the principles of Chevalier's Behavior Engineering Model (BEM). By correlating interview insights with BEM's framework, we could accurately identify and assess the behavioral factors and competencies within the lab. This integration allowed us to draw meaningful connections between the qualitative data from interviews and the theoretical constructs of BEM.

Root Cause Analysis

Fishbone Diagram

Adapted from Watkins (2012) Figure 3B.1 p.199

Utilizing Chevalier’s (2023) Behavior Engineering Model (BEM) alongside a fishbone diagram, we conducted a comprehensive cause analysis to identify key factors contributing to superior lab safety performance. This integrated approach allowed for a structured categorization of essential elements such as Information, Resources, Incentives, Knowledge/Skills, Capacity, and Motives. The fishbone diagram visually mapped these factors, revealing their interconnections

and impact on lab safety performance. This combination of methodologies offered a holistic view, resulting in actionable insights for sustaining high safety performance. The analysis derived that the exceptional lab safety record is a result of structured processes that instill best practices and diverse communication strategies, all within a community-focused environment where safety culture is paramount and actively nurtured.

Intervention Selection

Siko’s I2 Matrix

Table 6

Table 7

We then utilized Siko's I-2 Intervention Matrix (2013) to assess the feasibility and prioritize the selected interventions. The matrix’s design, focusing on the invasiveness (Table 6) and impact (Table 7) of each intervention, was instrumental in distilling the complex issue of lab safety into a manageable and quantifiable framework. The quadrant layout of the matrix enabled us to effectively prioritize interventions, ensuring that our choices were effective and balanced. The dual-axis framework of the matrix, evaluating both immediate impact and organizational invasiveness, was crucial in our decision-making process, helping us to identify solutions that were not only effective but achievable.

Recomendations

Following our detailed analysis, we advised giving precedence to the interventions outlined below and have proposed subsequent actions for their implementation.

Formalize Mentorship Program

Refine & further develop mentorship program with clear roles, matched pairings, safety trainings, and a feedback system for continuous enhancement.

Develop a structured framework for the mentorship program, outlining roles, expectations, and goals for both mentors and mentees.
Implement a mentor-mentee matching system based on expertise and learning needs to ensure optimal knowledge transfer.

Build a Succession and Recruitment Plan

Male officer suggesting steps to success

Conduct talent assessments, develop targeted recruitment strategies, that align with organizational dynamics.

Conduct talent assessments to identify current employees with leadership potential and align their development with future organizational needs.
Develop a recruitment strategy that focuses on acquiring skills essential for critical roles, ensuring alignment with the lab’s long-term goals.

Perform Reciprocal Safety Audits

Partner for reciprocal safety audits, regularly exchange and analyze findings for continuous safety improvement.

Establish partnerships with reputable external organizations to conduct reciprocal safety audits.
Develop a comprehensive audit checklist that covers all aspects of lab safety, tailored to industry benchmarks and best practices.

Lab Safety Performance Rubric

The lessons learned from this needs analysis offer BSU the opportunity to establish a standardized blueprint for exemplary lab safety. Based on the policies and practices in AML, we have created a rubric which the COEN Safety Team could use to guide and measure other labs.

References:

Chevalier, R. (2003, May/June). Updating the behavior engineering model. Performance Improvement, 42(5), 8–13. https://doi.org/10.1002/pfi.4930420504

Siko, J. P. (2013). Using the I² Intervention Matrix to Select the Best Course of Action. Performance Improvement, 52(10), 23–26. https://doi.org/10.1002/pfi.21378