Login / Signup

Developing evidence-based resources for evaluating postgraduate trainees in the biomedical sciences.

Jacqueline E McLaughlinRebekah L LaytonPaul B WatkinsRobert A NicholasKim L R Brouwer
Published in: PloS one (2022)
Postgraduate trainees elevate the academic strength of institutions by conducting research, promoting innovation, securing grant funding, training undergraduate students, and building alliances. Rigorous and systematic program evaluation can help ensure that postgraduate training programs are achieving the program's intended outcomes. The purpose of this project was to develop evidence-based evaluation tools that could be shared across federally funded biomedical training programs to enhance program evaluation capacity. This manuscript describes the evidence-based process used to determine program evaluation needs of these programs at a research-intensive university. Using a multi-phased sequential exploratory mixed methods approach, data were collected from trainees, employers, leaders, and program directors. Data analyses included document analysis of program plans, inductive coding of focus groups and interviews, and descriptive analysis of surveys. Two overarching categories-Trainee Skills and Program Characteristics-were identified including six themes each. Program directors prioritized communication, social and behavioral skills, and collaboration as the trainee skills that they needed the most help evaluating. Furthermore, program directors prioritized the following program characteristics as those that they needed the most help evaluating: training environment, trainee outcomes, and opportunities offered. Surveys, interview scripts, and related resources for the categories and themes were developed and curated on a publicly available website for program directors to use in their program evaluations.
Keyphrases
  • quality improvement
  • healthcare
  • public health
  • primary care
  • clinical trial
  • machine learning
  • type diabetes
  • metabolic syndrome
  • mental health
  • big data
  • virtual reality
  • deep learning
  • medical students