Using team cognitive work analysis to reveal healthcare team interactions in a birthing unit
Bibliographic record
Abstract
Cognitive work analysis (CWA) as an analytical approach for examining complex sociotechnical systems has shown success in modelling the work of single operators. The CWA approach incorporates social and team interactions, but a more explicit analysis of team aspects can reveal more information for systems design. In this paper, Team CWA is explored to understand teamwork within a birthing unit at a hospital. Team CWA models are derived from theories and models of teamwork and leverage the existing CWA approaches to analyse team interactions. Team CWA is explained and contrasted with prior approaches to CWA. Team CWA does not replace CWA, but supplements traditional CWA to more easily reveal team information. As a result, Team CWA may be a useful approach to enhance CWA in complex environments where effective teamwork is required. PRACTITIONER SUMMARY: This paper looks at ways of analysing cognitive work in healthcare teams. Team Cognitive Work Analysis, when used to supplement traditional Cognitive Work Analysis, revealed more team information than traditional Cognitive Work Analysis. Team Cognitive Work Analysis should be considered when studying teams.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".