Development of a Video Coding Scheme for Understanding Human-Computer Interaction and Clinical Decision Making
Bibliographic record
Abstract
The usability of healthcare information technology has become a major issue in health informatics. There have been many reports of systems that have been deemed unusable by end users such as clinicians and a growing body of usability studies have been reported in the literature. The issue of how to fruitfully analyze and code usability study data in a meaningful way that can lead to optimized and more efficient systems has remained to be fully detailed. In this paper we describe our work in developing and organizing a principled video coding scheme that builds from our previous work in a couple of areas. We include video coding categories we have developed for understanding problems and issues with human-computer interaction. In addition, we integrate this coding scheme with categories we have used to characterize human cognition, such as clinical reasoning and decision making, in isolation of technology use. The resultant new scheme thus incorporates coding categories that can used to evaluate both usability issues (applying categories from human-computer interaction) and human cognition, in order to assess the impact of technology on clinical reasoning and decision making.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".