Qualitative investigation of trace-based communication: how are traces conceptualised in healthcare teamwork?
Bibliographic record
Abstract
OBJECTIVES: This interview-based qualitative study aims to explore how healthcare providers conceptualise trace-based communication and considers its implications for how teams work. In the biological literature, trace-based communication refers to the non-verbal communication that is achieved by leaving 'traces' in the environment and other members sensing them and using them to drive their own behaviour. Trace-based communication is a key component of swam intelligence and has been described as a critical process that enables superorganisms to coordinate work and collectively adapt. This paper brings awareness to its existence in the context of healthcare teamwork. DESIGN: Interview-based study using Constructivist Grounded Theory methodology. SETTING: This study was conducted in multiple team contexts at one of Canada's largest acute-care teaching hospitals. PARTICIPANTS: 25 clinicians from across professions and disciplines. Specialties included surgery, anesthesiology, psychiatry, internal medicine, geriatrics, neonatology, paramedics, nursing, intensive care, neurology and emergency medicine. INTERVENTION: Not relevant due to the qualitative nature of the study. PRIMARY AND SECONDARY OUTCOME: Not relevant due to the qualitative nature of the study. RESULTS: The dataset was analysed using the sensitising concept of 'traces' from Swarm Intelligence. This study brought to light novel and unique elements of trace-based communication in the context of healthcare teamwork including focused intentionality, successful versus failed traces and the contextually bounded nature of the responses to traces. While participants initially felt ambivalent about the idea of using traces in their daily teamwork, they provided a variety of examples. Through these examples, participants revealed the multifaceted nature of the purposes of trace-based communication, including promoting efficiency, preventing mistakes and saving face. CONCLUSIONS: This study demonstrated that clinicians pervasively use trace-based communication despite differences in opinion as to its implications for teamwork and safety. Other disciplines have taken up traces to promote collective adaptation. This should serve as inspiration to at least start exploring this phenomenon in healthcare.
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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 | 0.037 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".