Cost vs. Benefit: What does NVivo Video Analysis of EMR Simulations Add to Our Understanding of User Experience?
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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