Practical Tips for using a Human Library approach In medical education
Bibliographic record
Abstract
A Human Library is a structured event that brings people from different groups together. It simulates the format of a customary library, with 'Readers' borrowing 'Books', who are human volunteers sharing their lived experiences and perspectives. Rooted in principles of social psychology, Human Libraries provide opportunities for Books and Readers to interact in meaningful dialogue. The goal of each interaction is to give the Reader new understanding of the Book's life. The Human Library was originally developed as a strategy to challenge prejudice through conversation and personal connection, but the approach is remarkably versatile. We repurposed it for a medical education context in order to provide learners in medical school with information and inspiration, particularly about rural life and rural medicine. We organized and held two Human Library events where pre-medical and undergraduate medical students (Readers) engaged in dialogue with rural physicians (Books). However, the strategy could be used to address a wide variety of challenging subjects where the potential Readers are biased or lack experience. This article draws upon research literature and our own experiences of running Human Library events to give practical advice for other organizations who might want to use this novel approach in medical education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.027 | 0.012 |
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 source (direct Gemma or distilled Codex), 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".