Just in Time Teaching (JiTT) Infographics App for Teacher Development
Bibliographic record
Abstract
During clinical clerkships, medical students credit residents for enhancing their clinical knowledge. Residents can spend at least 25% of their time teaching medical students, and many training programs consider resident-as-teacher skills a core competency. Despite this key role, many residents lack adequate instructions and training in teaching and mentoring skills and methodologies.Given the growing demand to enhance the existing curriculum among our residents and faculty (concomitant with requirements set forth by accreditation bodies), faculty development is moving to Just in Time Teaching models for content delivery. The ubiquitous use and availability of smartphones and connectivity through applications (apps) has tremendous potential to enhance the ability of trainees and faculty to supervise learners on their clinical teams with evidence-based knowledge and skills across the continuum of medical education. Geographically dispersed academic health systems, which continue to grow, require access to educational resources to guide diverse teaching needs of clinicians.The Just in Time Teaching (JiTT) Infographics app was created by combining a pedagogical approach derived from the SAMR (substitution, augmentation, modification, redefinition) technological conceptual framework. The JiTT Infographics app is a novel teaching tips approach that delivers evidence-based clinically relevant teaching tips to trainees and clinical faculty in their environment and can be used alone or adapted for resident-as-teacher programs.Each JiTT infographic tool supports clinical teachers' access to faculty development with an asynchronous digital experience strategy to engage busy teachers in a geographically distributed medical education network.1 Educational content was developed by medical educators, clinician educators, and residents. Each JiTT tool is also downloadable as a PDF to explain foundational and specialty-specific clinical teaching tips during didactic sessions. JiTTs can be saved in a “Favorites” category to ease finding a JiTT in the moment of need.Foundational JiTT tools include brief podcasts to support learners who prefer listening to content. Foundational teaching principles include setting expectations, questioning techniques, feedback and coaching, and bedside teaching. Specific clinical teaching techniques include content pertaining to internal medicine, family medicine, pediatrics, obstetrics and gynecology, surgery, psychiatry, and neurology, as well as sub-specialties. In addition, categories focused on teaching wellness, quality, social justice, and research principles are included. Optional review questions are provided in each category for users to self-assess their acquired knowledge.The JiTT Infographics app is available in Google Play and the Apple Store. As of April 2022, the app has been downloaded by 3406 unique users across 90 countries, with the largest numbers of downloads from the United States (N=2337), Phillippines (N=179), Mexico (N=169), Canada (N=98), and Saudia Arabia (N=68). Preliminary analysis of app activity highlights the foundational teaching category, which includes: Domains of Social Determinants of Health; 5 Micro Skills: Precept with Limited Time; and Questioning as an Effective Teaching Skill. The results to date support a global interest in utilizing technology to increase accessibility to open access resources. As of April 2022, a brief video on YouTube (July 2021) that guides users on how to apply the JiTT in real-time teaching settings has been viewed 462 times. In March 2022, the app was accredited as an “enduring product,” enabling interprofessional users to obtain selected continuing education credits at no cost. The app as an evidence-based JiTT teaching tool is an outcome in itself, as it redfines technology for faculty development.Future efforts will focus on app evaluation by capturing end-user feedback via brief structured feedback surveys and more in-depth analyses of learner engagement metrics to determine the outcomes and effectiveness of the app. Requests for translation to other languages are in the pipeline, as are additional external collaborators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".