Conducting Phenomenological Research in Medical Education
Bibliographic record
Abstract
Phenomenology originated as a philosophy that qualitative methodologists later translated and adapted into empirical research practices. Also referred to as applied phenomenology, this qualitative methodology aims to describe and understand a phenomenon as perceived by those who have experienced it. It conveys and shares lived experiences, making it a valuable method for medical education research. Although its popularity is rising within medical education, limited literature provides insights into how phenomenology can be applied in this field. This paper aims to prepare novice researchers by offering a guide for developing a phenomenological study. We begin by describing the similarities and differences between the two key types of phenomenology, transcendental (descriptive) and hermeneutic (interpretive) phenomenology. This is followed by the core tenets of the methodology that researchers should know, including the concepts of phenomena and essences. We conclude with practical steps for conducting phenomenological research, starting at the beginning of the research process with learning about prominent scholars in the field to data analysis at the end. Understanding the key principles of this methodology can ensure its accurate use and representation in medical education. Additionally, by providing an introductory guide to phenomenology, we hope to make this methodology more accessible to novice health professionals and scholars in medical education.
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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 | 0.115 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| 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".