'Tell me what you know': Written recall as a window into illness script quality

Illness scripts (mental representations of disease knowledge) are central to diagnostic expertise, yet structured methods to examine their quality across medical training are lacking. This exploratory study investigated whether textual indicators derived from free-recall protocols could reveal aspects of illness script quality in medical students.

In this cross-sectional study, students from years one through six at seven Brazilian medical schools completed a free-recall task for chronic obstructive pulmonary disease (COPD) and myocardial infarction (MI) after a progress test. Written responses were segmented into idea units (IUs) and analysed for accuracy (correct idea units), scope (number of disease dimensions), organisation (balanced adjusted ratio of clustering) and encapsulation (higher-order summary terms). Differences across training years were examined using Kruskal-Wallis and Dunn tests.

Accuracy and scope increased significantly across training years for both diseases (median correct ideas for COPD: 3 in year 1 versus 14 in year 5, p < 0.01; for MI: 8 versus 18, p < 0.01). Most gains occurred between years 1-2 and 3, with limited differentiation thereafter. Organisation increased from 0.31 in year 1 to 0.61 in year 5 for COPD and from 0.28 to 0.60 for MI (range -1 to +1; both p < 0.01). Encapsulated and incorrect ideas were infrequent at all training levels. Progress test scores correlated positively with the number of correct ideas (COPD rho = 0.56; MI rho = 0.46; p < 0.01).

Free-recall protocols analysed along theoretically grounded dimensions detected differences in accuracy, scope and organisation across training years. Constraints, such as a time-imposed ceiling effect and limited sensitivity to encapsulated knowledge, should guide future methodological refinement. Despite its preliminary nature, this methodology may contribute to research on clinical expertise by offering a novel approach to studying illness scripts across training levels.