Triangulating qualitative research and computer transaction logs in health information studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The aim of this paper is to outline a triangulated methodology for studying usage of electronic health information systems which combines the quantitative data accrued from computer logs with qualitative data from in‐depth interviews and observation. Design/methodology/approach The appropriate methods and inherent issues are reviewed from the literature, with an emphasis on qualitative research. The work of the authors is then highlighted, showing how qualitative methods can inform log analysis. Findings The paper suggests from the review that it is not only possible but also extremely fruitful to combine quantitative and qualitative data to interpret user behaviour. Originality/value The methods used by the group, known as “deep log analysis”, are innovative, and the attempt both to discuss these and to provide concrete examples from this research provides its originality.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it