Qualitative Research in Veterinary Medical Education: Part 2—Carrying Out Research Projects
Bibliographic record
Abstract
This is the second of two articles that together comprise an orientation and introduction to qualitative research for veterinary medical educators who may be new to research, or for those whose research experience is based on the quantitative traditions of biomedicine. In the first article ( Part 1—Principles of Qualitative Design), we explored the types of research interests and goals suited to qualitative inquiry and introduced the concepts of research paradigms and methodologies. In this second article, we move to the strategies and actions involved in conducting a qualitative study, including selection and sampling of research sites and participants, data collection and analysis. We introduce some guidelines for reporting qualitative research and explore the ways in which qualitative research is evaluated and the findings applied. Throughout, we provide illustrative examples from veterinary and human medical education and suggest useful resources for further reading. Taken together, the two articles build an understanding of qualitative research, outline how it may be conducted, and equip readers with an improved capacity to appraise its value.
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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 | 0.065 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".