Use of Techno-Anthropologic Approaches in Studying Technology--induced Errors.
Bibliographic record
Abstract
In this book chapter the authors review several Techno-Anthropologic approaches that can be used to improve the quality and safety of health information technology (HIT) by eliminating or reducing the incidence and occurrence of technology-induced errors. Technology-induced errors arise from interactions between health professionals, patients and/or HIT (i.e., software and hardware) and lead to a medical error. Techno-Anthropologic methods can be used to address these types of medical errors before they occur. In this book chapter they are discussed in the context of: (a) how they can be applied to identifying technology-induced errors and (b) how this information can be used to design and implement safer HIT. Important in this chapter is a review of several methods: traditional ethnography, rapid assessment of clinical information systems, video ethnography and photovoice as they are applied to the discovery of potential (i.e., near misses) and actual (i.e., mistakes) technology-induced errors.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".