Understanding discharge communication behaviours in a pediatric emergency care context: a mixed methods observation study protocol
Bibliographic record
Abstract
BACKGROUND: One of the most important transitions in the continuum of care for children is discharge to home. Optimal discharge communication between healthcare providers and caregivers (e.g., parents or other guardians) who present to the emergency department (ED) with their children is not well understood. The lack of policies and considerable variation in practice regarding discharge communication in pediatric EDs pose a quality and safety risk for children and their parents. METHODS: The aim of this mixed methods study is to better understand the process and structure of discharge communication in a pediatric ED context to contribute to the design and development of discharge communication interventions. We will use surveys, administrative data and real-time video observation to characterize discharge communication for six common illness presentations in a pediatric ED: (1) asthma, (2) bronchiolitis, (3) abdominal pain, (4) fever, (5) diarrhea and vomiting, and (6) minor head injury. Participants will be recruited from one of two urban pediatric EDs in Canada. Video recordings will be analyzed using Observer XT. We will use logistic regression to identify potential demographic and visit characteristic cofounders and multivariate logistic regression to examine association between verbal and non-verbal behaviours and parent recall and comprehension. DISCUSSION: Video recording of discharge communication will provide an opportunity to capture important data such as temporality, sequence and non-verbal behaviours that might influence the communication process. Given the importance of better characterizing discharge communication to identify potential barriers and enablers, we anticipate that the findings from this study will contribute to the development of more effective discharge communication policies and interventions.
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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 | 0.067 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".