Preliminary adaptation of the systems thinking for everyday work cue card set in a US healthcare system: a pragmatic and participatory co-design approach
Bibliographic record
Abstract
INTRODUCTION: Healthcare is a highly complex adaptive system, requiring a systems approach to understand its behaviour better. We adapt the Systems Thinking for Everyday Work (STEW) cue cards, initially introduced as a systems approach tool in the UK, in a US healthcare system as part of a study investigating the feasibility of a systems thinking approach for front-line workers. METHODS: The original STEW cards were adapted using consensus-building methods with front-line staff and safety leaders. RESULTS: Each card was examined for relevance, applicability, language and aesthetics (colour, style, visual cues and size). Two sets of cards were created due to the recognition that systems thinking was relatively new in healthcare and that the successful use of the principles on the cards would need initial facilitation to ensure their effective application. Six principles were agreed on and are presented in the cards: Your System outlines the need to agree that problems belong to a system and that the system must be defined. Viewpoints ensure that multiple voices are heard within the discussion. Work Condition highlights the resources, constraints and barriers that exist in the system and contribute to the system's functions. Interactions ask participants to understand how parts of the system interact to perform the work. Performance guides users to understand how work can be performed daily. Finally, Understanding seeks to promote a just cultural environment of appreciating that people do what makes sense to them. The two final sets of cards were scored using a content validity survey, with a final score of 1. CONCLUSIONS: The cards provide an easy-to-use guide to help users understand the system being studied, learn from problems encountered and understand the everyday work involved in providing excellent care. The cards offer a practical 'systems approach' for use within complex healthcare systems.
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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 | 0.046 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".