Selecting the best nurses with Talogy Modular System for Healthcare
Improve the quality of the nursing workforce with a predictive assessment
A large American kidney care company engaged Talogy to help them improve the quality of their nursing workforce. Specifically, the project’s aim was to design, validate, and implement a customized selection tool that could be used to hire employees into the nursing positions at the organization’s locations. The primary objective of the project was to design, implement, and maintain selection processes that screen out “risky” candidates and help the organization to place qualified individuals into jobs in an accurate and fair manner.
A criterion-related validation was conducted across nine different nursing modalities. This study was conducted to customize and evaluate the performance of the selection process used for hiring Clinical Nurse positions.
In this study, nursing incumbents from nine different nursing modalities completed the customized Talogy Modular System for Healthcare (formerly known as NurseFit®) assessment. Supervisors provided job performance ratings on the same individuals.
Job performance was rated on several domains: task performance, contextual performance, and global performance. Task performance refers to performance on tasks specified in the job requirements, whereas contextual performance describes employees’ behaviors that are beneficial to the organization but outside of established job responsibilities. Global ratings assess supervisor’s overall impression of the individual. Overall performance is a combination of all these domains. In addition to performance ratings, supervisors were asked two additional questions regarding the incumbents’ overall job performance:
Knowing what you know now about this individual, how likely is it that you would hire him/her again?
How likely is it that this individual will leave the organization in the next two years?
Customized scoring logic is used to place individuals into one of four recommendation categories based on their assessment scores: Poor Fit, Potential Fit, Good Fit, and Very Good Fit. The overall mean job performance ratings are shown across all four recommendation categories.
As shown, individuals who scored higher on the assessment were rated significantly higher on job performance compared to those individuals who scored lower. Higher assessment scores are associated with higher job performance ratings. Thus, we can conclude that candidates who score higher on the Talogy Modular System for Healthcare assessment are more likely to perform better on the job.
We were also interested in examining how the assessment related to promotability. Supervisors were asked the likelihood of promotion for the incumbent in the next two years.
Generally, a rating of “Very Likely” to be promoted is indicative of an overall excellent job performance record. Of those individuals who passed the assessment, 14% were rated as “Very Likely” to be promoted, whereas of the individuals who failed the assessment, only 6% were rated as “Very Likely” to be promoted. As such, individuals who pass the Customized profiles are twice as likely to be recommended for promotion compared to those who failed.
Screening out the poor performers
One of the key aims of the assessment is to screen out the highest risk candidates. Of those individuals in the “worst job performance” group, nearly half (48%) would have been eliminated from contention for the position by the assessment. When asked “would you rehire this individual?” nearly every nurse who was rated as “Very Unlikely” to rehire, would have failed the assessment and never have been hired.
The customized Talogy Modular System for Healthcare assessment is predictive of success for nurses in a variety of modalities across the organization. Moreover, results indicate that the customized scoring will improve the quality of the hires by screening out the most unfavorable candidates. This assessment was tailored to accurately address the specific needs of the client and provide customized scoring logic to meet performance objectives for selecting the best nurse candidates.
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