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.
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 | 0.166 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".