Small sample sizeStudents were tested at only 3 stations

Not generalizable because only one medical scenario and team was analyzed.

It also presents a series of first test findings but not as yet the results of improvements initiated by using the instrument.The indicators were considered relevant and important but too many in number. Therefore, a possibility to use the instrument in three separate parts, relating respectively to the period before, during and after a crisis, should be offered.

A larger sample size may have demonstrated a difference, but our study suggests that it is equally effective to use a mix of (case-based learning) CBL and (simulation-based learning) SBL.

The opportunistic selection process that was used in order to include individualsin the comparison group was open to selection bias.Relatively small number of participants in the training resulted in reduced power in the subgroup analysis.External validity of the study was poor because the training course that was developed cannot be used by hospitals worldwide without being adjusted first.

It is possible that students, completing the two, 29-item post-course RIPLS surveys, one directly after another, did not read the negatively worded items carefully and demonstrated response set bias.

Whereas simulations can demonstrate RaDES efficacy and weaknesses, only funding an agency to prepare teams and field test them will demonstrate its practicability.implementation faces logistical, funding, and political obstacles.

36 out of 73 organizations of New Zealand participated and 12 North American organizations [relatively small sample size]Instances of difference across questions in participants’ answers might be possible due to mis-readings or misinterpretations.

Methods applied are useful

Future work: In future we plan to improve the traffic model by implementation of multilane roads, by more precise capturing of a human behavior (trust, panics). Second future plans’ direction refers to implementation of additional scenarios and DSS types. Scenario 5. Navigation system with flood dynamics forecast. Scenario 6. The scenario extends the last one with forecast of road load dynamics. The forecast can be obtained by a simulation with traffic models or by use of central collaborative information storage. Scenario 7. based on the system-optimal approach to planning the evacuation Scenario 8. with capability for agents to communicate with their friends by mobile phones in order to share the information on flood dynamics.