Getting everyone to the table: exploring everyday and everynight work to consider ‘latent social threats’ through interprofessional tabletop simulation
Bibliographic record
Abstract
In this methodological intersection article, we describe how we developed a new variation of the established tabletop simulation modality, inspired by institutional ethnography (IE)-informed principles. We aimed to design and conduct pilot implementations of this innovative tabletop simulation modality, which focused uniquely on everyday and everynight work, along with the factors that govern that work. In so doing, we aimed to develop a modality and preliminary findings that researchers and educators can use to simulate healthcare practices across longer episodes of care (i.e., time scales of hours or an entire day) and to detect the 'latent social threats' that can emerge during interprofessional clinical care.An interprofessional team designed tabletop simulation scenarios of interprofessional challenges during transfers of care on a labour and delivery (L&D) unit. Within each scenario, participants provided real-time explanations for their work and associated drivers, both independently and as a team. Thus, we combined 'think-aloud' and simulation principles to design tabletop simulation scenarios to elicit healthcare professionals' descriptions of how they collaborate in their work on the L&D unit. We completed a total of five tabletop simulations with eight participants (obstetricians, N = 2; midwives, N = 2; nurses, N = 5).The conversations stimulated by the tabletop simulation scenarios and debriefs allowed us to generate a preliminary understanding of the texts that govern and organize clinicians' everyday work processes. We generated data about longitudinal, multi-hour work processes in a condensed timeline, with opportunities to pause and probe, and with reduced focus on individual practitioner's competence.We believe our innovative tabletop simulation approach allowed us to examine clinical work in ways no other simulation permits. Participants described how the scenarios opened a productive dialogue between professional groups and suggested this simulation-based approach might contribute to enhanced interprofessional understanding and cultural change. We suggest that others can adapt our low-resource approach to understand clinicians' everyday work and to map how this work is governed by documents, like policies, with the end goal of facilitating system change and managing latent social threats.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".