A meta‐study analysing the discourses of discourse analysis in health professions education
Bibliographic record
Abstract
INTRODUCTION: Discourse analysis has been used as an approach to conducting research in health professions education (HPE) for many years. However, because there is no one 'right' interpretation of or approach to it, quite what discourse analysis is, how it could or should be used, and how it can be appraised are unclear. This ambiguity risks undermining the trustworthiness and coherence of the methodology and any findings it produces. METHOD: A meta-study review was conducted to explore the current state of discourse analysis in HPE, to guide researchers engaging using the methodology and to improving methodological, analytical and reporting rigour. Structured searches were conducted, returns were filtered for inclusion and 124 articles critically analysed. RESULTS: Of 124 included articles, 64 were from medical education, 51 from nursing and 9 were mutli-disciplinary or from other HPE disciplines. Of 119 articles reporting some sort of data, 50 used documents/written text as the sole data source, while 27 were solely based on interview data. Foucault was the most commonly cited theorist (n = 47), particularly in medical education articles. The quality of articles varied: many did not provide a clear articulation what was meant by discourse, definitions and methodological choices were often misaligned, there was a lack of detail regarding data collection and analysis, and positionality statements and critiques were often underdeveloped or absent. DISCUSSION: Seeking to address these many lacunae, the authors present a framework to facilitate rigorous discourse analysis research and transparent, complete and accurate reporting of the same, to help readers assess the trustworthiness of the findings from discourse analysis in HPE. Scholars are encouraged to reflect more deeply on the applications and practices of discourse analysis, with the ultimate aim of ensuring more breadth and depth when using discourse analysis for understanding and constructing meaning in our field.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".