CARDA: Guiding document analyses in health professions education research
Bibliographic record
Abstract
INTRODUCTION: Documents, from policies and procedures to curriculum maps and examination papers, structure the everyday experiences of health professions education (HPE), and as such can provide a wealth of empirical information. Document analysis (DA) is an umbrella term for a range of systematic research procedures that use documents as data. METHODS: A meta-study review was conducted with the aims of describing the current state of DA in HPE, guiding researchers engaging in DA and improving methodological, analytical and reporting rigour. Structured searches were conducted, returns were filtered for inclusion and the 115 remaining articles were critically analysed for their use of DA methods and methodologies. RESULTS: There was a significant increase in the number of articles reporting the use of DA over time. Sixty-three articles were single method (DA only), while the others were mixed methods research (MMR). Overall, there were major lacunae in terms of why documents were used, how documents were identified, what the authors did and what they found from the documents. This was particularly apparent in MMR where DA reporting was typically poorer than the reporting of other methods in the same paper. DISCUSSION: Given these many lacunae, a framework for reporting on DA research was developed to facilitate rigorous DA research and transparent, complete and accurate reporting of the same, to help readers assess the trustworthiness of the findings from document use and analysis in HPE and, potentially, other domains. It was also noted that there are gaps in HPE knowledge that could be addressed through DA, particularly where documents are conceptualised as more than passive holders of information. Scholars are encouraged to reflect more deeply on the applications and practices of DA, with the ultimate aim of ensuring more substantive and more rigorous use of documents 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 | 0.021 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".