Doing our work better, together: a relationship-based approach to defining the quality improvement agenda in trauma care
Bibliographic record
Abstract
BACKGROUND: Trauma care represents a complex patient journey, requiring multidisciplinary coordinated care. Team members are human, and as such, how they feel about their colleagues and their work affects performance. The challenge for health service leaders is enabling culture that supports high levels of collaboration, co-operation and coordination across diverse groups. We aimed to define and improve relational aspects of trauma care at Gold Coast University Hospital. METHODS: We conducted a mixed-methods collaborative ethnography using the relational coordination survey-an established tool to analyse the relational dimensions of multidisciplinary teamwork-participant observation, interviews and narrative surveys. Findings were presented to clinicians in working groups for further interpretation and to facilitate co-creation of targeted interventions designed to improve team relationships and performance. FINDINGS: We engaged a complex multidisciplinary network of ~500 care providers dispersed across seven core interdependent clinical disciplines. Initial findings highlighted the importance of relationships in trauma care and opportunities to improve. Narrative survey and ethnographic findings further highlighted the centrality of a translational simulation programme in contributing positively to team culture and relational ties. A range of 16 interventions-focusing on structural, process and relational dimensions-were co-created with participants and are now being implemented and evaluated by various trauma care providers. CONCLUSIONS: Through engagement of clinicians spanning organisational boundaries, relational aspects of care can be measured and directly targeted in a collaborative quality improvement process. We encourage healthcare leaders to consider relationship-based quality improvement strategies, including translational simulation and relational coordination processes, in their efforts to improve care for patients with complex, interdependent journeys.
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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 | 0.118 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.021 | 0.051 |
| Scholarly communication | 0.034 | 0.034 |
| Open science | 0.008 | 0.031 |
| Research integrity | 0.007 | 0.018 |
| 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".