A Snapshot of Doctoral Training in Epidemiology: Positioning Us for the Future

Exploring postgraduate epidemiology competencies: Preparing our students for a post-COVID world

OBJECTIVE

This research sought consensus from both experts and graduates on postgraduate epidemiology competencies.

METHODS

In 2021, a two-round online survey using a modified Delphi method was undertaken exploring competencies across six domains. Focus groups were conducted with recent postgraduate epidemiology graduates to assess their viewpoints on learning experiences and employability.

RESULTS

Forty-one experts participated in the first Delphi round. Nineteen factors reached consensus (>70% agreement) for importance and feasibility after two survey rounds in the following domains: general epidemiologic methods/concepts (n=8/13), advanced analytic/statistical skills (n=2/7), applied epidemiology/specialised fields (n=1/4), professional/transferrable skills (n=5/14), general public health knowledge/skills (n=2/4), independent research and work-integrated learning (n=1/3). Nine graduates participated in focus groups. A main theme was the substantial value gained in undertaking a dissertation, acknowledging its benefit for applying research skills and for networking opportunities.

CONCLUSIONS

To ensure that high-quality epidemiological research and practice continues, we need consensus on the set of essential skills required of graduating students.

IMPLICATIONS FOR PUBLIC HEALTH

Competencies for postgraduate epidemiology students require periodic review to safeguard a workforce that can meet emerging challenges and work across academia, research, policy, and practice.

Development of a learning health system science competency assessment to guide training and proficiency assessment

Introduction

Learning health systems (LHS) science is fundamentally a transdisciplinary field. To capture the breadth of the competencies of an LHS scientist, AHRQ and national experts defined a series of 42 competencies across seven domains that support success. Clinicians, researchers, and leaders who are new to the LHS field can identify and prioritize proficiency development among these domains. In addition, existing leaders and researchers will assemble teams of experts who together represent the LHS science domains. To serve LHS workforce development and proficiency assessment, the AHRQ‐funded ACCELERAT K12 training program recruited domain experts and trainees to define and operationalize items to include in an LHS Competency Assessment to support emerging and existing LHS scientists in prioritizing and monitoring proficiency development.

Methods

Sequential interviews with 18 experts iteratively defined skills and tasks to illustrate stage in proficiency, and its progression, for each of 42 competencies in the seven LHS expertise domains: systems science; research questions and standards of scientific evidence; research methods; informatics; ethics of research and implementation in health systems; improvement and implementation science; and engagement, leadership, and research management. An educational assessment expert and LHS scientist refined the assessment criteria at each stage to use parallel language across domains. Last, current trainees reviewed and pilot tested the assessment and the LHS Competency Assessment was further refined using their feedback. The assessment framework was informed by Bloom's revised taxonomy of educational objectives (Anderson and Krathwohl, A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives, 2001) where learning progresses from recalling, defining, understanding, and awareness at the lower levels of the taxonomy, to applying and adopting and finally to creating, designing, and critiquing at the upper levels of the taxonomy. We also developed assessment criteria that could be used for longer term assessment of direct performance. Van der Vleuten et al. (Best Pract Res Clin Obstetr Gynaecol. 2010;24(6):703‐719) define longer term direct assessment methods as assessment that occurs over a period ranging from weeks to even years and involves multiple assessment methods and exposure to the learner's work over an extended period.

Results

This experience report describes the content of the LHS Competency Assessment. For each domain and competency, the assessment lists examples of evidence to support expertise at each level of proficiency: no exposure; foundational (awareness/understanding); emerging (early application); and proficient (application with a high level of skill). Trainees begin with baseline standard assessment tables, where they can indicate no exposure or mark the foundational and emerging skills with which they have competence. For domains where foundational and emerging skills have been achieved, users can move on to assessment tables that list evidence of proficiency.

Conclusion

The LHS Competency Assessment offers consistent, graded criteria across the seven LHS domains to guide trainees and mentors to evaluate progress from no experience to foundational knowledge, emerging proficiency, and proficiency. The assessment can also be used to design training and mentoring for those newly exposed to LHS science and for those with key expertise who wish to expand LHS expertise.

The Measurement Error Elephant in the Room: Challenges and Solutions to Measurement Error in Epidemiology

Measurement error, although ubiquitous, is uncommonly acknowledged and rarely assessed or corrected in epidemiologic studies. This review offers a straightforward guide to common problems caused by measurement error in research studies and a review of several accessible bias-correction methods for epidemiologists and data analysts. Although most correction methods require criterion validation including a gold standard, there are also ways to evaluate the impact of measurement error and potentially correct for it without such data. Technical difficulty ranges from simple algebra to more complex algorithms that require expertise, fine tuning, and computational power. However, at all skill levels, software packages and methods are available and can be used to understand the threat to inferences that arises from imperfect measurements.