Using Comparative Cognitive Work Analysis to Identify Design Priorities in Complex Socio-Technical Systems
Bibliographic record
Abstract
Health care can be categorized as a complex socio-technical system. Often, similar projects or systems experience very different outcomes during implementation. To better understand the differentiating success factors when comparing projects or systems, we argue there is a need to systematically compare complex socio-technical systems. While Cognitive Engineering offers many methods for analyzing complex socio-technical systems, such as Cognitive Work Analysis, few methods support a comparison paradigm. We propose using Cognitive Work Analysis and using it to compare two similar socio-technical systems through Comparative Cognitive Work Analysis. Through parallel phases to Cognitive Work Analysis, the comparative method allows for the identification of differentiating factors, which we call Junctions, and allows practitioners to identify design opportunities to introduce success interventions into existing designs. Future work will involve further development of this concept and its application to healthcare problems.
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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 | 0.092 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.023 | 0.011 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".