Visual Data in Education and Health Research: Historical Reflections and Current Prognostications
Bibliographic record
Abstract
This commentary serves to explore the relationship between photography and medicine since the 1800s, in order to establish a contemporary link between the two, and thus to act as a renewed invitation for pedagogical consideration for educators and researchers. Three themes are developed: first, there is a strong link between the advancement of photography as a technical field and the advancement of medical practices and education since the 1800s in a way which invites renewed consideration. Second, there is a strong mandate to consider the explosion of visual images in our everyday and global virtual landscapes vis a vis social media for the ongoing purpose of excellent standards for education and research. And finally, the field of narrative medicine has gained significant recognition, bringing the arts into clinical practice and training of clinicians, further suggesting the value and importance of visual data in the field of education and research. These 3 themes are the building blocks for an exploration of the value of visual data, here to stay in virtual and public educational domains. Educators in health sciences and health-related studies are invited to consider the value and strategies of visual data towards curriculum development, as transformative tools, and in regards to their potential not only for education, but also for clinical practice and research.
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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 | 0.040 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.009 | 0.061 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".