Article Figures & Data
Figures
- Figure 1.
The figure depicts our proposed application of the Model of Domain Learning (MDL)(Alexander 2003) to the development of expertise in arboriculture, specifically related to tree risk assessment. The model follows the authors’ three stages of learning (i.e., initial, second, final) as a person goes from a novice to an expert.
Tables
- Table 1.
A list of key terms that have been outlined in the literature which are used throughout the review.
Term Definition Expert An individual who is well versed in a particular subject matter and is recognized by others as having expertise in their field (Skjong and Wentworth 2001). Expertise Exceptional performance in a given field (Krosnick 1990). Professional judgment A reasonable decision that has given full and fair consideration to all the appropriate information, that is based upon principled and reasoned analysis and the best available science and expertise, and that complies with applicable laws (Haas 2002). Professional An individual that has previous education and training related to a specific profession which allows them to perform associated tasks at a relatively high level (Cambridge University Press 2020). Professionalism The actions, conduct, and mindset of an individual in a professional setting (Virginia Tech 2020). Risk assessment A systematic process for identifying hazards, determining if there is a target, evaluating the potential for risk, and then proposing some sort of mitigation if it is warranted by the situation (Ball and Ball-King 2014). Tree risk assessment Evaluating the level of risk (low to very high) associated with a tree and its likelihood to fail and impact a person or property (Pokorny 2003). Risk perception The subjective assessment of the probability of a specified type of accident happening, and how concerned we are with the consequences (Sjöberg et al. 2004). Emotional intelligence The skills associated with emotion-based information that takes into account the measurement of related abilities (Mayer et al. 2003). Risk governance A construct for the manner in which public risks are dealt with, specifically those that are complicated and clouded by uncertainty (Renn and Klinke 2013). - Table 2.
Areas of future research that have been identified as having the potential to add to our current understanding of tree risk assessment.
Future research need Utility to tree risk assessment Additional studies that evaluate the influence of previous experience, training, and industry accreditation. Help guide future training programs and industry certifications to better fit the skills needed to perform tree risk assessments more accurately and consistently. A better understanding as to what qualifies someone as a professional and what criteria should be evaluated to determine their ability to perform tree risk assessments. This information can be used to help homeowners and other decision makers make more informed choices regarding the individuals that they hire to assess and manage tree risk. Further examining how tree-related judgments are made and the individuals who make the decisions. Such insight has the potential to better our understanding of the relationship between expertise and tree risk management. Explore the effect that risk perception, personal bias, and other subjective inputs have on the risk assessment process and the subsequent mitigation. Provide a better understanding of how and why tree-related decisions are made and determine potential approaches to counter such subjective inputs through education and training.
Jump to section
Related Articles
Cited By...
- No citing articles found.