No Faculty Required: Use of a Health Literacy Low Inference Self-Assessment Measure to Promote Behavior Change

Interprofessional Active Learning for Chronic Pain: Transforming Student Learning From Recall to Application

Chronic pain (CP) affects over 50 million Americans daily and represents a unique challenge for healthcare professionals due to its complexity. Across all health professions, only a small percentage of the curriculum is devoted to treating patients with CP. Unfortunately, much of the content is delivered passively via lecture without giving students an opportunity to practice the communication skills to effectively treat patients in the clinic. An interprofessional team of health educators identified 5 essential messages that students frequently struggle to convey to patients with CP. Those messages were based on interprofessional and profession-specific competencies to treat patients with CP from the International Association for the Study of Pain. The 5 messages highlighted the importance of (1) therapeutic alliance, (2) consistent interdisciplinary language, (3) patient prognosis, (4) evidence for pain medicine, surgery, and imaging, and (5) early referral to the interprofessional team. For each message, the team summarized relevant research supporting the importance of each individual message that could serve as a foundation for didactic content. The team then developed active learning educational activities that educators could use to have students practice the skills tied to each message. Each learning activity was designed to be delivered in an interprofessional manner.

Identifying and classifying medical jargon through analysis of recorded standardized patient encounters

INTRODUCTION

Learning effective communication, particularly jargon avoidance, is important for medical training. Standardized methods exist to identify jargon but there is room to further refine those methods to define medical jargon and expand categorization. This project aims to classify jargon words as having a plain language alternative or not, to both standardize the definition of jargon and as a foundation for teaching jargon avoidance.

METHODS

We analyzed 123 transcribed encounters between standardized patients and medical students to quantify and categorize medical jargon using a stepwise process based on published literature. This process eliminated common words (based on New Dale-Chall list) without distinct medical meaning. Uncommon words and words with distinct medical meanings found in Stedman's Medical Dictionary were considered jargon. Jargon words were cross-referenced with the Plain Language Thesaurus to identify plain language alternatives.

RESULTS

This process identified 310 jargon words, 102 with plain language alternative from the 123 encounters.

CONCLUSION

We objectively classified jargon into distinct categories with a novel focus on jargon words with and without plain language alternatives.

PRACTICE IMPLICATIONS

This objective classification system serves as an important step in gaining a comprehensive understanding of jargon use which is essential to improving and teaching communication skills.

Anatomy Education Environment Measurement Inventory (AEEMI): a cross-validation study in Malaysian medical schools

BACKGROUND

The Anatomy Education Environment Measurement Inventory (AEEMI) evaluates the perception of medical students of educational climates with regard to teaching and learning anatomy. The study aimed to cross-validate the AEEMI, which was previously studied in a public medical school, and proposed a valid universal model of AEEMI across public and private medical schools in Malaysia.

METHODS

The initial 11-factor and 132-item AEEMI was distributed to 1930 pre-clinical and clinical year medical students from 11 medical schools in Malaysia. The study examined the construct validity of the AEEMI using exploratory and confirmatory factor analyses.

RESULTS

The best-fit model of AEEMI was achieved using 5 factors and 26 items (χ ² = 3300.71 (df = 1680), P < 0.001, χ ²/df = 1.965, Root Mean Square of Error Approximation (RMSEA) = 0.018, Goodness-of-fit Index (GFI) = 0.929, Comparative Fit Index (CFI) = 0.962, Normed Fit Index (NFI) = 0.927, Tucker-Lewis Index (TLI) = 0.956) with Cronbach's alpha values ranging from 0.621 to 0.927. Findings of the cross-validation across institutions and phases of medical training indicated that the AEEMI measures nearly the same constructs as the previously validated version with several modifications to the item placement within each factor.

CONCLUSIONS

These results confirmed that variability exists within factors of the anatomy education environment among institutions. Hence, with modifications to the internal structure, the proposed model of the AEEMI can be considered universally applicable in the Malaysian context and thus can be used as one of the tools for auditing and benchmarking the anatomy curriculum.