Background

High-quality PhD nursing student mentorship facilitates student and program success. Extant literature recommends evaluating and improving mentorship to foster optimal PhD student development. However, a comprehensive measure capturing all aspects of mentorship salient to PhD nursing student wellbeing and success is not available.

Objectives

The purpose of this pilot study was to develop a new instrument - the Collaboration for Leadership and Innovation in Mentoring (CLIM) - for quantifying important components of PhD student mentorship in nursing, and to preliminarily test its psychometric properties (content validity, sensitivity, test-retest reliability).

Design

The study employed a cross-sectional design.

Setting

The CLIM instrument was administered to nursing PhD students at a public state university in the United States.

Participants

Sixteen nursing PhD students at various stages in their degree progression completed the instrument.

Methods

PhD nursing students developed unique items based on qualitative data collected by the University using an Appreciative Inquiry framework. Seven nursing and non-nursing experts with experience in PhD mentorship evaluated content validity. After revisions, the final 44-item instrument was administered at two time points (one month apart) to allow assessment of test-retest reliability. Test-retest reliability was evaluated using Spearman-rank correlations and data from students with ≥1 year of experience with their mentor.

Results

Response rates were 94% for both administrations (n = 16). The instrument's overall Content Validity Index (CVI) was 0.91 (p = 0.05). Test-retest analyses resulted in high correlations (r = 0.91, p < 0.001), further supporting reliability of the CLIM instrument.

Conclusions

Preliminary evidence suggests that the CLIM instrument is a reliable instrument of PhD mentorship in nursing. However, additional testing in larger and more diverse graduate student populations is needed to evaluate internal consistency reliability, among other psychometric properties.