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Signal Detection Theory: Enabling Work Near the Edge

Profile image of Gregory HowellGregory Howell

Abstract

Occupational accidents are unquestionably wasteful and non-value adding events in any system of production. Safeguarding construction workers from occupational hazards, whether arising from traumatic, ergonomic, and/or exposure accidents, is part and parcel of the lean construction ideal of waste elimination. Howell et al. (2002) proposed a new approach to understand construction accidents based on Rasmussen's theory of cognitive systems engineering. One aspect of the model focused on worker training to recognize hazards (unsafe conditions). The underlying assumption here is that workers will always recall what constitutes a safe or unsafe situation as well as respond to perceived or actual risks in the same manner. Therefore, a methodology to assess worker sensitivity to unsafe conditions and risk orientation is needed. This paper proposes a methodology based on Signal detection theory that was originally developed as an assessment technique for tasks requiring the detection of...

Key takeaways
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  1. Signal Detection Theory (SDT) enhances worker safety by assessing sensitivity to unsafe conditions in construction.
  2. Construction safety statistics highlight 15 deaths per 100,000 workers, exceeding the manufacturing sector's rate.
  3. Training methods must improve to acknowledge the contextual nature of hazard recognition among workers.
  4. Rasmussen's model emphasizes training workers to navigate and recover from hazardous conditions effectively.
  5. Future research should focus on enlarging the safe zone and improving hazard recognition training methodologies.
Figures (5)
Se ae ee ee ee lee a | el elle = es OU lel = ge The original model as proposed by Rasmussen is shown in Figure 1. As shown, Rasmussen divided the work environment to three zones. Zone I, which is the region enclosed by the “Boundary of unconditionally safe behavior”, “Organizational Boundary to Economic Failure”, and “Individual Boundary to Unacceptable Workload”, is considered the safe zone (Rasmussen et al. 1994, Rasmussen 1997).
Se ae ee ee ee lee a | el elle = es OU lel = ge The original model as proposed by Rasmussen is shown in Figure 1. As shown, Rasmussen divided the work environment to three zones. Zone I, which is the region enclosed by the “Boundary of unconditionally safe behavior”, “Organizational Boundary to Economic Failure”, and “Individual Boundary to Unacceptable Workload”, is considered the safe zone (Rasmussen et al. 1994, Rasmussen 1997).
In SDT, the signal indicator or strength is assumed to have a normal distribution (argued using the central limit theorem). It follows then that the information in Table 1 could be graphically represented as shown in Figure 2. Xc, shown in Figure 2, represents the critical level where an observer decides the nature of a signal. In other words, Xc represents the “mental” cut-off the observer uses to decide whether to say ‘yes’ there is a signal (a hit), or ‘no’ there is noise (correct rejection).
In SDT, the signal indicator or strength is assumed to have a normal distribution (argued using the central limit theorem). It follows then that the information in Table 1 could be graphically represented as shown in Figure 2. Xc, shown in Figure 2, represents the critical level where an observer decides the nature of a signal. In other words, Xc represents the “mental” cut-off the observer uses to decide whether to say ‘yes’ there is a signal (a hit), or ‘no’ there is noise (correct rejection).
Table 2: The SDT matrix for detection of unsafe conditions in construction As explained before, d’ and beta (current and optimum) are the SDT parameters or metrics used to assess a worker’s sensitivity to unsafe and safe conditions as well as the inclination to consider a condition as safe or unsafe. High values of d’ indicate high sensitivity in differentiating safe from unsafe conditions. Conversely, low values of d’ indicate that a worker needs more training to recognize the two conditions. — _ _ a _
Table 2: The SDT matrix for detection of unsafe conditions in construction As explained before, d’ and beta (current and optimum) are the SDT parameters or metrics used to assess a worker’s sensitivity to unsafe and safe conditions as well as the inclination to consider a condition as safe or unsafe. High values of d’ indicate high sensitivity in differentiating safe from unsafe conditions. Conversely, low values of d’ indicate that a worker needs more training to recognize the two conditions. — _ _ a _
— The result for one of the workers is shown in Table 4. Based on the responses, the number of hits, misses, false alarms, and correct rejections are determined and converted to probabilities. For example, if a question portrayed a safe condition and the worker’s response was ‘unsafe condition’ or ‘I don’t know’, then this was considered a false alarm. If the question portrayed an unsafe condition and the worker’s response was ‘safe condition’ or ‘I don’t know’, then this was considered a miss. A hit or a correct rejection results when the worker correctly identifies an unsafe condition as unsafe or a safe condition as safe.
— The result for one of the workers is shown in Table 4. Based on the responses, the number of hits, misses, false alarms, and correct rejections are determined and converted to probabilities. For example, if a question portrayed a safe condition and the worker’s response was ‘unsafe condition’ or ‘I don’t know’, then this was considered a false alarm. If the question portrayed an unsafe condition and the worker’s response was ‘safe condition’ or ‘I don’t know’, then this was considered a miss. A hit or a correct rejection results when the worker correctly identifies an unsafe condition as unsafe or a safe condition as safe.
The above example illustrates how the sensitivity and risk orientation of a worker can be determined. This information sets a benchmark against which the effectiveness of new training can be assessed. Essentially, this information would make it possible to determine if a worker’s sensitivity and risk orientation towards safe and unsafe conditions increased, decreased, or remained unchanged. Ultimately, the use of SDT will result in increasing workers abilities to judge the boundary beyond which work is no longer safe. Table 5: Sample survey analysis results
The above example illustrates how the sensitivity and risk orientation of a worker can be determined. This information sets a benchmark against which the effectiveness of new training can be assessed. Essentially, this information would make it possible to determine if a worker’s sensitivity and risk orientation towards safe and unsafe conditions increased, decreased, or remained unchanged. Ultimately, the use of SDT will result in increasing workers abilities to judge the boundary beyond which work is no longer safe. Table 5: Sample survey analysis results

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