Qualitative Research in Veterinary Medical Education: Part 1—Principles of Qualitative Design
Bibliographic record
Abstract
Qualitative methodologies are relative newcomers to health sciences education research. While they may look very different to their quantitative counterparts in terms of size and scope, when well-applied they offer a fresh perspective and generate valuable research findings. Although qualitative research is being increasingly conducted in veterinary medical education, there are few contextualized resources to assist those who would like to develop their expertise in this area. In this article, we address this by introducing the principles of qualitative research design in a veterinary medical education context. Drawing from a range of contemporary resources, we explore the types of research goals and questions that are amenable to qualitative inquiry and discuss the process of formulating a worthwhile research question. We explain what research paradigms are and introduce readers to some of the methodological options available to them in qualitative research. Examples from veterinary medical education are used to illustrate key points. In a second companion article, we will focus on the decisions that need to be made regarding data sampling, collection, and analysis. We will also consider how qualitative research is evaluated, and discuss how qualitative findings are applied. Taken together, the two articles build an understanding of qualitative research, illuminate its potential to contribute to the scholarship of teaching and learning in veterinary medical education, and equip readers with an improved capacity to appraise its value.
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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 | 0.027 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".