O013 / #557: LIBER8 GLASS GOALS PROJECT: A NOVEL METHOD FOR ENSURING DAILY, INDIVIDUALIZED PATIENT-GOAL SETTING AND TEAM COMMUNICATION.
Bibliographic record
Abstract
Aims & Objectives: Daily goals checklists have been shown to improve patient care and team communication in the critical care setting. Institutional surveys showed that staff found previous checklists ineffective. The objective of this study was to evaluate a novel daily goals checklist using the glass door of the patient rooms, as an easily visible communication tool, to set patient goals and track progress in a Pediatric Intensive Care Unit (PICU). Methods: We used the Pronovost’s 4 E’s model (Engage, Educate, Execute, and Evaluate) to implement the Glass Goals. The Glass Goals are a template on patient doors, for goal setting in specific domains of sedation, breathing, circulation, diet, early mobilization, fluids, and family goals. Content was created in collaboration with PICU staff, leadership, and families, and rolled out over 1 month. Goal setting and team communication were assessed using pre- and post-implementation rounding audits and surveys, and a post-implementation uptake assessment.Results: Pre-implementation rounding audits on 49 patients found the frequency of goal setting was 40.2% across all domains excluding family goals. Median rounding time was 11:46 (IQR = 8:25). Post-implementation, Glass Goals completion was 94.6% (N = 74). Post-implementation surveys and rounding audits are ongoing. Conclusions: Preliminary analyses indicate that implementation of the Glass Goals leads to improved uptake of patient goal setting and team communication. Future analyses will assess post-implementation goal setting during rounds, rounding time, and perceptions of the Glass Goals by parents and HCPs using surveys. Acknowledgements: Hannah Zimmerman, Grace Lamond, Data Collection Assistants; McMaster Family Advisory Council; Dr. Samara Zavalkoff
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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 | 0.025 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".