Low-touch approach empowering clinical teams to improve the medical on-call communication experience
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Team functioning is integral to providing high quality patient care. Improving communication during on-call medical coverage requires a level of individual engagement that can be challenging to achieve in large organisations, particularly in a climate of high population healthcare needs and health human resource limitations. This project represents a novel approach through engaging care providers in addressing on-call communication culture using a systems approach and quality improvement methodology. METHODS: Factors that influence the interdisciplinary experience of making, receiving and responding to calls about patient care were identified. An asynchronous action series addressed the key drivers of a good call experience. RESULTS: The Good Call Action Series was developed collaboratively by interdisciplinary teams. Six multidisciplinary teams across seven specialties participated over 5 months. A modified team effectiveness score demonstrated a 13% improvement on completion of the action series. CONCLUSION: System thinking can be effectively applied to the complexity of the on-call experience for all members of the healthcare team. Clinical teams can develop team functioning skills and solve complex on-call communication issues with minimal support and without structured quality improvement training. Low-touch, time-efficient activities designed and delivered using quality improvement methodology can effectively address team-based care delivery challenges.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".