More and Less Effective Updating: The Role of Trajectory Management in Making Sense Again
Bibliographic record
Abstract
This study examines how updating—the process of revising provisional sensemaking to incorporate new cues—occurs within teams during unexpected events. I compare how 19 teams of emergency department staff managed the same unexpected event (a broken piece of equipment) in a medical simulation scenario. Using a microethnographic approach to analyze video recordings of these teams, I conduct a fine-grained examination of how updating takes place and find considerable variation in its effectiveness across teams. I show that the effectiveness of updating depends not only on how teams remake sense but also on how they engage in trajectory management, balancing the work of updating with their ongoing work (in this case, patient care). Trajectory management practices related to monitoring cues and managing engaging tasks facilitated effective updating and allowed teams to detect and identify the problem caused by the broken piece of equipment and correct it before it led to serious consequences. More-effective teams monitor and rapidly interpret cues, confirming them with others and evaluating changes over time; they then investigate cues, develop plausible explanations, and quickly test them, monitoring cues for feedback. Less-effective teams fail to monitor and confirm cues with others, overlook or misinterpret cues, and delay investigating cues and developing plausible explanations; they also delay testing explanations, often being sidetracked by patient care tasks.
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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 | 0.011 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".