Assessing Mentor Academy Program Effectiveness using Mixed Methods.

Area of Science:

• Medical Education
• Translational Science
• Mentorship Programs

Background:

• Effective mentorship is crucial for academic health centers (AHCs) and clinical translational science institutes (CTSI).
• Assessing the impact of formal mentor training programs on mentor competence and relationship quality is essential for optimizing research productivity.
• Existing evaluation methods, such as surveys, may not capture the full spectrum of program effectiveness.

Purpose of the Study:

• To evaluate the impact of an AHC mentor academy program on mentor competence and the mentor-mentee relationship.
• To compare the effectiveness of a mixed-methods approach versus surveys alone in assessing mentorship program outcomes.
• To identify specific areas of mentor development and potential gender-based differences in mentoring practices.

Main Methods:

• A mixed-methods approach was employed, combining validated surveys with reflective writings.
• Twenty biomedical research faculty mentors from a CTSI participated in the study.
• Quantitative survey data were analyzed for statistical significance and compared with qualitative insights from reflective writings.

Main Results:

• Significant improvements were observed in participants' confidence in mentor skills, overall mentoring quality, and meeting mentee expectations.
• Male mentors reported providing constructive feedback and assisting with goal strategies more frequently than female mentors.
• Reflective writings corroborated most survey findings but also contradicted two survey-based conclusions, underscoring the limitations of solely quantitative data.

Conclusions:

• The AHC mentor academy program effectively enhanced mentor competence and improved mentor-mentee relationships.
• A mixed-methods approach provides a more comprehensive understanding of mentoring program effectiveness than surveys alone.
• Future evaluations of mentoring programs should consider incorporating qualitative data to capture nuanced outcomes and validate quantitative findings.