Cost vs. Benefit: What does NVivo Video Analysis of EMR Simulations Add to Our Understanding of User Experience?
Bibliographic record
Abstract
Improving healthcare using phased, iterative and participatory methods requires time and resources to do comprehensively. The reality, particularly for practitioners, is that constraints related to human resources, cost and time may impact the rigor of data collection and analysis. Under such conditions, project teams may rely on tacit knowledge and expertise to fill in potential gaps in understanding and validate design decisions. But what kind of insights might emerge if we were freed from such constraints, and given the time to study a context in more detail? Our research group explored this question by using Computer Assisted Qualitative Data Analysis Software (NVivo) and qualitative research coding methods to analyze a sample of video data collected from a series of electronic medical record (EMR) workflow simulations that were originally used to support EMR implementation in a pediatric hospital. The results from the NVivo video analysis revealed some details not previously captured by initial data analysis methods, but at significant resource cost. A comparison of video analysis methods, findings and respective costs are compared and discussed in the context of design development and implementation.
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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 | 0.223 | 0.486 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".