User image mismatch in anaesthesia alarms: a cognitive systems analysis
Bibliographic record
Abstract
In this study, principles of Cognitive Systems Engineering are used to better understand the human-machine interaction manifesting in the use of anaesthesia alarms. The hypothesis is that the design of the machine incorporates built-in assumptions of the user that are discrepant with the anaesthesiologist's self-assessment, creating 'user image mismatch'. Mismatch was interpreted by focusing on the 'user image' as described from the perspectives of both machine and user. The machine-embedded image was interpreted through document analysis. The user-described image was interpreted through user (anaesthesiologist) interviews. Finally, an analysis was conducted in which the machine-embedded and user-described images were contrasted to identify user image mismatch. It is concluded that analysing user image mismatch expands the focus of attention towards macro-elements in the interaction between man and machine. User image mismatch is interpreted to arise from complexity of algorithm design and incongruity between alarm design and tenets of anaesthesia practice. PRACTITIONER SUMMARY: Cognitive system engineering principles are applied to enhance the understanding of the interaction between anaesthesiologist and alarm. The 'user image' is interpreted and contrasted from the perspectives of machine as well as the user. Apparent machine-user mismatch is explored pertaining to specific design features.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".