Use of a Daily Goals Checklist for Morning ICU Rounds
Bibliographic record
Abstract
OBJECTIVE: To understand the perspectives and attitudes of ICU clinicians about use of a daily goals checklist on rounds. DESIGN: Our three data collection methods were as follows: (1) Field observations: two investigators conducted field observations to understand how and by whom the daily goals checklist was used for 80 ICU patient rounds over 6 days. (2) Document analysis: The 72 completed daily goals checklists from observed rounds were analyzed using mixed methods. (3) Interviews: With 56 clinicians, we conducted semistructured individual and focus-group interviews, analyzing transcripts using a qualitative descriptive approach and content analysis. Triangulation was achieved by a multidisciplinary investigative team using two research methods and three data sources. SETTING: Fifteen bed closed ICU in a tertiary care, university-affiliated hospital. PATIENTS: Medical-surgical ICU patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Field observations: The daily goals checklist was completed for 93% of observed rounds, largely by residents (86%). The champion of the verbal review was commonly a resident (83%) or medical student (9%). Document analysis: Domains with high completion rates included ventilation, sedation, central venous access, nutrition, and various prophylactic interventions. Interviews: The daily goals checklist enhanced communication, patient care, and education. Nurses, physicians, and pharmacists endorsed its enhancement of interdisciplinary communication. It facilitated a structured, thorough, and individualized approach to patient care. The daily goals checklist helped to identify new patient care issues and sparked management discussions, especially for sedation, weaning, and medications. Residents were prominent users, finding served as a multipurpose teaching tool. CONCLUSIONS: The daily goals checklist was perceived to improve the management of critically ill patients by creating a systematic, comprehensive approach to patient care and by setting individualized daily goals. Reportedly improving interprofessional communication and practice, the daily goals checklist also enhanced patient safety and daily progress, encouraging momentum in recovery from critical illness. Daily goals checklist review prompted teaching opportunities for multidisciplinary learners on morning rounds.
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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 | 0.019 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".