“Call Me by My Name”: Improving Communication with Family Members at the Bedside via the Caregiver Identification Badge
Bibliographic record
Abstract
BACKGROUND: Patient and family experience are integral to the care that we provide. In the pediatric hospital setting, multiple family members are directly involved in patient care. We identified the need for greater caregiver name recognition at The Hospital for Sick Children, Toronto, ON. OBJECTIVE: We aimed to improve communication between healthcare providers and families via the optimization of caregiver identification badges. METHODS: We used a qualitative, narrative study design to explore perceptions surrounding caregiver identification badges via unstructured interviews. RESULTS: We identified key hospital and family stakeholders. Unstructured interviews supported the theory that badge optimization could improve communication and patient care. Our initiative, however, was abruptly interrupted by the emergence of the COVID-19 pandemic. CONCLUSION: Communication with patients and families is crucial across medical disciplines. The optimization of caregiver identification badges to facilitate the use of preferred names and pronouns will ultimately lead to the more effective and safer delivery of high-quality care.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".