There is little evidence available to help us understand what psychological and psychosocial factors contribute the most to the process of composing teams with the best chances of successfully living and working together for long durations.  Within social and organizational psychology research there are some studies around “group composition” or putting together project/ mission teams within already-existing, larger work or social groups. However, with the outcome of interest being the whole performance of the sub-group or team rather than individual performance, research has more difficulty obtaining sufficient sample sizes to finely identify contributing psychosocial factors.  Researchers need a large number of teams to effectively test predictors, and teams are both harder to gain access to or recruit and harder to research due to changing membership (e.g. teams break-up, exchange team members, or exclude team members for various operational reasons).  We do know that some psychological factors (like social support) predict team health and performance in some working conditions but not in others, and at different levels at times within a team’s lifespan together.  Current research evidence is entirely in populations that are dissimilar to astronauts and offer limited value in predicting their (astronaut’s) team performance.  Further, the performance outcomes used in these studies rarely resemble astronaut performance requirements and are studied in contexts that are nothing like long duration space missions.  Additionally, most work groups or teams are composed from a larger set of individuals than NASA has available to choose from when composing crews (e.g. most organizations the size of NASA/JSC have dozens of engineering employees to pick from for a particular role within one project team, but NASA usually has only a couple of qualified astronauts to pick from to fill particular role, like robotics lead, for an expedition team).  We do not know what, if any, factors should be considered when composing a crew of astronauts from a small set of qualified astronauts for a long-duration space flight mission. At this point, research cannot advise operations what factors are most likely to ensure that the best decisions are made regarding crew composition. An algorithm is needed to describe both what factors are important and what the trade-offs are in terms of risk of performance decrements for ignoring one psychological factor for a higher priority psychological factor or operational requirement.

b) Target for Closure

An algorithm to inform crew composition decisions that incorporates the validated set of measures (from Gap 2)  and key indicators (from Gap 1) that predict outcomes for different mission durations and provides effective teaming for autonomous long duration missions. Specifically, validated measures and indicators that have research evidence supporting their predictive (criterion-related validity) for task performance, teamwork, and psychosocial adaptation over time among populations who are similar to and doing work similar to astronauts. An algorithm that weights individual characteristics (e.g., KSAs), roles, positions, mission attributes and scenarios and informs crew composition decisions.

c) Metrics for interim progress

1. Identify the set of measures, key indicators, and key mission attributes that can be used for the algorithm to inform decisions on crew composition for autonomous exploration missions (40%); 2. Develop validation criteria relating psychological and behavioral measures to team function and mission scenarios and attributes (25%); 3. Vet with SMEs (10%); 4. Develop report that integrates algorithm with research evidence, knowledge of critical mission-related factors (e.g., crew size, volume, habitat design)with operational requirements (10%); 5. Vet with SMEs (10%); 6. Determine if model is needed (5%).

d) Approach

The conclusions/results from tasks will determine the metrics used to quantify the completion of the Gap. The amount of resources and effort to close this gap will be quite considerable. It will be imperative to select the most accurate measures, key indicators, and mission attributes that will provide the foundation of the proposed algorithm. In order to ensure this is done successfully, BHP research will take careful considerations of all options, consult with SMEs and take the time to thoroughly assess all viewpoints. Determining effect sizes for work similar to the astronauts (the actual work, the duration of missions, performance past 6 months) will be no small task and adequate time must be taken into consideration when working towards closing this gap. As mentioned above, it will be difficult and time-intensive locating and researching with similar groups; however, it is a necessity in order to produce sufficient information to inform the algorithm for operations. Once the tasks are satisfied and complete, we will be able to say confidently which indicators are going to create the most suitable team composition. This gap will take approximately 7-12 years to close (dependent on information obtain in pursuit of prior gaps).