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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".