Predicting success on a special forces selection course: Identifying entry tests and establishing standards

Selection of personnel for employment within the Australian Army Special Forces involves the successful completion of a four week course that assesses the ability to function effectively under great physical and mental strain. To be deemed suitable to attempt the course and to maximize successful completion, candidates must first pass the Special Forces Entry Test (SFET). In addition, candidates must successfully pass barrier assessments that are applied at the beginning of the selection course. The aims of the present investigation were firstly, to determine if an effective baseline pass score could be set for each SFET assessment in order to panel candidates for the selection course; and secondly, to determine the reliability of predicting selection course outcome (pass/fail)
from a combination of SFET assessments and initial selection course barrier assessments.
One hundred and four candidates completed the revised SFET and commenced the Commando Selection and Training Course (CSTC 01/11). The physical capacity assessments of the revised SFET included vertical jump height, sit and reach test of flexibility, push-ups, 7-stage sit-up test, heaves, agility test, the beep test (VO2max), yo-yo test, 5-km pack march (carrying a 40 kg load) and a 400-m swim. At the beginning of the CSTC candidates were also required to successfully pass two additional barrier assessments, a 3.2-km battle run (carrying 7 kg webbing and a weapon) and a 20-km pack march
(carrying a weapon and 28 kg distributed in a pack and webbing).
For a baseline pass score to be effective, it must prevent any false negative classifications (resulting in the exclusion of candidates who would pass CSTC) and limit false positive classifications (allowing candidates to commence CSTC when they are likely to fail). Analysis revealed that when a baseline pass score was set at the level of the lowest candidate that passed (ensuring no false negative classifications), a very high number of false positive classifications also resulted. Therefore, the current study suggests that a baseline pass score
for each individual SFET assessment would be ineffective at excluding candidates from CSTC who are likely to fail.
Statistical modeling analysis to show the reliability of multiple SFET assessments to predict CSTC outcome revealed several SFET assessments to be associated with a greater chance of success. Candidates with a higher maximal aerobic power (VO2max), that completed the 5-km and 20-km marches quicker, who could complete more push-ups and achieved a higher 7 Stage sit-up level were more likely to pass the 20-km march assessment and successfully complete the CSTC. However, these models allowed for an unacceptable number of false negative and false positive classifications.
The SFET assessments shown to be significant predictors of success on the CSTC were analyzed further to identify baseline pass scores that, when applied in combination, provided greater utility in reducing false negative predictions. By this approach, candidates who passed one or more of the following criteria; push-ups (=66 repetitions), 7-stage sit-up (= Stage 5) and 5-km pack march (=45 min 30 s) resulted in zero false negative classifications. While this approach was able to limit the potential for false negative classifications, false positive classifications were still high. However, these findings present
the opportunity to propose SFET pass / fail standards for future validation.
There are several proposed reasons for the low predictability of the CSTC outcome based on the SFET assessments. Firstly, over half the candidates that failed were withdrawn from the CSTC for non-physical reasons, including withdrawn at own request, medical withdrawal, and board of studies – other. Factors likely to be behind these reasons for withdrawals include the candidate’s cognitive and psychological state, their musculoskeletal profile, and their skill and technical based competencies. Including an objective measure of
these factors in future work may help to improve the prediction of CSTC outcomes.