Meaning making about performance: A comparison of two specialty feedback cultures

Specialty trainees often struggle to understand how well they are performing, and feedback is commonly seen as a solution to this problem. However, medical education tends to approach feedback as acontextual rather than located in a specialty-specific cultural world. This study therefore compares how specialty trainees in surgery and intensive care medicine (ICM) make meaning about the quality of their performance and the role of feedback conversations in this process.

We conducted a qualitative interview study in the constructivist grounded theory tradition. We interviewed 17 trainees from across Australia in 2020, eight from ICM and nine from surgery, and iterated between data collection and analytic discussions. We employed open, focused, axial and theoretical coding.

There were significant divergences between specialties. Surgical trainees had more opportunity to work directly with supervisors, and there was a strong link between patient outcome and quality of care, with a focus on performance information about operative skills. ICM was a highly uncertain practice environment, where patient outcome could not be relied upon as a source of performance information; valued performance information was diffuse and included tacit emotional support. These different 'specialty feedback cultures' strongly influenced how trainees orchestrated opportunities for feedback, made meaning of their performance in their day-to-day patient care tasks and 'patched together' experiences and inputs into an evolving sense of overall progress.

We identified two types of meaning-making about performance: first, trainees' understanding of an immediate performance in a patient-care task and, second, a 'patched together' sense of overall progress from incomplete performance information. This study suggests approaches to feedback should attend to both, but also take account of the cultural worlds of specialty practice, with their attendant complexities. In particular, feedback conversations could better acknowledge the variable quality of performance information and specialty specific levels of uncertainty.