The Subjective Habitability & Acceptability Questionnaire (SHAQ): Development and Validation

OBJECTIVE

Describe the development and validation of the Subjective Habitability & Acceptability Questionnaire (SHAQ).

BACKGROUND

Habitat area size, layout, and design may impact individual and team behavioral health and performance (BHP) outcomes in operational environments. However, there are no standardized measures of these relationships.

METHOD

SHAQ is a modular survey consisting of two 6-item scales: BHP Outcomes (Performance of Individual Activities, Performance of Group Activities, Mood, Psychological Stress, Sleep, and Social Interactions) and Habitability Moderators (Privacy, Social Density, Efficiency, Control, Comfort, and Convenience). We collected SHAQ data from NASA's Human Exploration Research Analog (HERA) crews (n = 19) in reference to multiple habitat areas (Sleep/Bedroom, Hygiene/Bathroom, Work/Office/Workshop, and Food Preparation/Kitchen/Galley) in the HERA operational environment, private hotel rooms, and individual home habitats.

RESULTS

SHAQ has high construct validity (single factor solutions, mean item factor loading = 0.760, mean % variance = 60.37), internal consistency and reliability (item mean α = 0.880, mean ω=0.894, mean ICC = 0.430), concurrent validity (mean item r with System Usability Scale = 0.42), and discriminant validity (e.g., significantly higher facilitation of group activities in HERA Work/Office/Workshop and Food Preparation/Kitchen/Galley areas vs. Hygiene/Bathroom and Sleep/Bedroom areas; significantly higher ratings of privacy, comfort, and convenience in hotel vs. HERA).

CONCLUSION

SHAQ is a reliable, valid, and sensitive measure of BHP impacts of habitat size and layout.

APPLICATION

SHAQ can be used to inform evidence-based recommendations and thresholds for habitat area size, layout, and design options to support individual and team BHP in operational environments.

Predicting Design-Induced Error on the Flight Deck : An Aircraft Engine Oil Leak Scenario

OBJECTIVE

To explore the types of errors that commercial pilots may make when trying to resolve a suspected engine oil leak using the interfaces currently available.

BACKGROUND

The decisions that pilots make often have to be made quickly and under time pressure, with the emphasis on avoiding critical situations from arising. To make the correct decisions, it is vital that pilots have accurate and up-to-date information available. However, interaction with flight deck interfaces may lead to error if they are not effectively designed.

METHOD

A hierarchical task analysis was conducted using evidence from pilot interview data to understand the pilots' typical response to a suspected engine oil leak scenario. This was used as the primary input into the Systematic Human Error Reduction and Prediction Approach (SHERPA).

RESULTS

A total of 108 possible errors were identified. The most common error type was a retrieval error, in which flight crews may retrieve the wrong information about the engine. A number of remedial measures are proposed to try and overcome such issues.

CONCLUSION

This analysis provides an initial starting point for identifying potential future design ideas that can assist the pilots in dealing with oil leaks.

APPLICATION

This work has identified the value of applying human error identification methodologies to the assessment of current flight deck processes surrounding engine oil leaks. The method presented permits the operational analysis of possible errors on the flight deck and facilitates the proposition of remedial measures to implement technological innovations that can mitigate error.

Defining and Managing the Preanalytical Phase With FMECA: Automation and/or "Human" Control

OBJECTIVE

Our scope is to provide methodological elements on how to manage effectively the preanalytical phase in the laboratory testing process, by objectively measuring the risk connected to the phases handled by man with respect to those managed by machines.

BACKGROUND

Preanalytical errors account for most of the mistakes related to laboratory testing and can affect patient care. Hence, it is necessary to manage the risk connected to the preanalytical phase, as required by certification and accreditation bodies. The risk assessment discloses the steps at greater risk and gives indications to make decisions.

METHOD

We have reviewed the state of art in the automation of the preanalytical phase, addressing needs and problems. We have used the proactive risk assessment methodology FMECA (Failure Mode, Effects, and Criticality Analysis) to identify the most critical phases in our preanalytical process and have calculated the risk associated.

RESULTS

The most critical phases were the human controlled ones. In particular, the highest risk indexes were associated to manual acceptance of test orders, identification of the patients, tube labeling, and sample collection.

CONCLUSION

Automation in the preanalytical phase is fundamental to replace, support, or extend the human contribution. Nevertheless each organization is different about workloads and competencies, so the most suitable management must be tailor-made in each context.

APPLICATION

We present a method by which each organization is able to find its best balance between automation and human contribution in the control of the preanalytical phase.

Using Tools to Help Us Think: Actual but Also Believed Reliability Modulates Cognitive Offloading

OBJECTIVE

A distributed cognitive system is a system in which cognitive processes are distributed between brain-based internal and environment-based external resources. In the current experiment, we examined the influence of metacognitive processes on external resource use (i.e., cognitive offloading) in such systems.

BACKGROUND

High-tech working environments oftentimes represent distributed cognitive systems. Because cognitive offloading can both support and harm performance, depending on the specific circumstances, it is essential to understand when and why people offload their cognition.

METHOD

We used an extension of the mental rotation paradigm. It allowed participants to rotate stimuli either internally as in the original paradigm or with a rotation knob that afforded rotating stimuli externally on a computer screen. Two parameters were manipulated: the knob's actual reliability (AR) and an instruction altering participants' beliefs about the knob's reliability (believed reliability; BR). We measured cognitive offloading proportion and perceived knob utility.

RESULTS

Participants were able to quickly and dynamically adjust their cognitive offloading proportion and subjective utility assessments in response to AR, suggesting a high level of offloading proficiency. However, when BR instructions were presented that falsely described the knob's reliability to be lower than it actually was, participants reduced cognitive offloading substantially.

CONCLUSION

The extent to which people offload their cognition is not based solely on utility maximization; it is additionally affected by possibly erroneous preexisting beliefs.

APPLICATION

To support users in efficiently operating in a distributed cognitive system, an external resource's utility should be made transparent, and preexisting beliefs should be adjusted prior to interaction.

Human Error as an Emergent Property of Action Selection and Task Place-Holding

OBJECTIVE

A computational process model could explain how the dynamic interaction of human cognitive mechanisms produces each of multiple error types.

BACKGROUND

With increasing capability and complexity of technological systems, the potential severity of consequences of human error is magnified. Interruption greatly increases people's error rates, as does the presence of other information to maintain in an active state.

METHOD

The model executed as a software-instantiated Monte Carlo simulation. It drew on theoretical constructs such as associative spreading activation for prospective memory, explicit rehearsal strategies as a deliberate cognitive operation to aid retrospective memory, and decay.

RESULTS

The model replicated the 30% effect of interruptions on postcompletion error in Ratwani and Trafton's Stock Trader task, the 45% interaction effect on postcompletion error of working memory capacity and working memory load from Byrne and Bovair's Phaser Task, as well as the 5% perseveration and 3% omission effects of interruption from the UNRAVEL Task.

CONCLUSION

Error classes including perseveration, omission, and postcompletion error fall naturally out of the theory.

APPLICATION

The model explains post-interruption error in terms of task state representation and priming for recall of subsequent steps. Its performance suggests that task environments providing more cues to current task state will mitigate error caused by interruption. For example, interfaces could provide labeled progress indicators or facilities for operators to quickly write notes about their task states when interrupted.