Focal Length Fluidity: Research Questions in Medical Education Research and Scholarship
Bibliographic record
Abstract
Research and scholarship in health professions education has been shaped by intended audience (i.e., producers vs users) and the purpose of research questions (i.e., curiosity driven or service oriented), but these archetypal dichotomies do not represent the breadth of scholarship in the field. Akin to an array of lenses required by scientists to capture images of a black hole, the authors propose the analogy of lenses with different focal lengths to consider how different kinds of research questions can offer insight into health professions research-a microscope, a magnifying glass, binoculars, and telescopes allow us to ask and answer different kinds of research questions. They argue for the relevance of all of the different kinds of research questions (or focal lengths); each provides important insight into a particular phenomenon and contributes to understanding that phenomenon in a different way. The authors propose that research questions can move fluidly across focal lengths. For example, a theoretical question can be made more pragmatic through asking "how" questions ("How can we observe and measure a phenomenon?"), whereas a pragmatic question can be made more theoretic by asking a series of "why" questions ("Why are these findings relevant to larger issues?"). In summary, only through the combination of lenses with different focal lengths, brought to bear through interdisciplinary work, can we fully comprehend important phenomena in health professions education and scholarship-the same way scientists managed to image a black hole.
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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 | 0.086 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.013 | 0.126 |
| Scholarly communication | 0.026 | 0.055 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".