Rich pictures: a companion method for qualitative research in medical education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
CONTEXT: Within the social sciences, researchers increasingly build on visual methods to explore complex phenomena and understand how people experience and give meaning to this complexity. Amongst the variety of visual methods available, rich pictures are beginning to gain traction in health professions education (HPE) research. APPROACH: A rich picture is a pictorial representation of a particular situation, including what happened, who was involved, how people felt, how people acted, how people behaved, and what external pressures they acted upon. Rich pictures expand our perspective; they may highlight connections, illuminate the big picture and reveal unexpected emotions. Although new methods bring excitement to the field, it is our responsibility to also be cautious and insightful about their limitations. Rich pictures are a method in evolution in HPE research, with many unknowns about what is possible and what is optimal. PURPOSE: In the current paper, we aim to map out the background, describe the process and share some reflective insights of using rich pictures as a data collection method.
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.
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 | 0.062 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it