Invited review: Qualitative research in dairy science—A narrative review
Bibliographic record
Abstract
The use of qualitative research in dairy science has increased considerably in recent years, providing the opportunity to inform research and practice. This review aims to enhance the accessibility of qualitative research among a range of audiences and specifically: (1) provide an overview of what qualitative research is and the value it can bring to scientific inquiries in the dairy context, (2) illustrate the emergence of qualitative dairy science research in the past 15 to 20 years, (3) outline the role of the researcher and key philosophical assumptions underlying qualitative research, (4) describe qualitative research approaches and methods used in dairy science research, and (5) highlight key aspects of qualitative inquiry used to ensure research trustworthiness. Qualitative approaches in dairy science enable researchers to understand myriad topics including stakeholder relationships, decision-making, and behaviors regarding dairy cattle management, animal welfare, and disease prevention and control measures. Approaches that were used often for qualitative data collection were individual interviews and focus groups, and variations of thematic analysis were common analytical frameworks. To assess public values, attitudes, and perceptions, mixed methods questionnaires that combined quantitative data with qualitative data from open-ended questions were used regularly. Although still used infrequently, action research and participatory approaches have the potential to bridge the research-implementation gap by facilitating group-based learning and on-farm changes. Some publications described the philosophical assumptions inherent to qualitative research, and many authors included reflexivity and positionality statements. Although a comprehensive description of strategies to meet trustworthiness criteria for qualitative research was uncommon, many publications mentioned certain aspects of trustworthiness, such as member checking, researcher triangulation, and the recording of reflexive notes. Qualitative research has been used to deepen our understanding of phenomena relevant to the dairy sector and has opened the door for a broad array of new opportunities. In addition to having merit on its own, qualitative research can guide, inform, and expand on quantitative research, and an understanding of the core pillars of qualitative research can foster interdisciplinary collaborations.
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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 | 0.050 | 0.212 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".