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Record W2803734896 · doi:10.1093/pch/pxy054.022

UNDERSTANDING DISCHARGE COMMUNICATION BEHAVIOURS IN PEDIATRIC EMERGENCY CARE

2018· article· en· W2803734896 on OpenAlexaffabout
Janet Curran, Andrea Bishop, Amy C. Plint

Bibliographic record

VenuePaediatrics & Child Health · 2018
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsChildren's Hospital of Eastern OntarioIzaak Walton Killam Health Centre
Fundersnot available
KeywordsEmergency departmentMedicineContext (archaeology)TriageBronchiolitisHospital dischargePediatricsMedical emergencyFamily medicineEmergency medicineNursingPsychiatryIntensive care medicine

Abstract

fetched live from OpenAlex

Abstract BACKGROUND One of the most important transitions in the continuum of care for children is discharge to home. However, optimal discharge communication between healthcare providers and parents who present to the emergency department (ED) with their children is not well understood. Current research regarding discharge communication is equivocal and predominantly focused on evaluating different delivery formats or strategies with little attention given to communication behaviours or the context in which the communication occurs. OBJECTIVES The aim of this mixed methods study was to characterize the process and structure of discharge communication in a paediatric ED context. DESIGN/METHODS Real-time video observation methods were used in two academic paediatric EDs in Canada. Parents who presented with their child to the ED with one of six illness presentations, a Canadian Triage Acuity Score of 3 to 5, and English speaking, were eligible to participate. All ED physicians, learners, and clinical staff members were also eligible to participate. Provider-parent communication was analyzed using the Roter Interaction Analysis System (RIAS) to code each utterance. Parent comprehensive was evaluated using a follow-survey 72 hours after discharge. RESULTS A total of 106 unique ED visits involving six illness presentations: abdominal pain (n=23), asthma (n=6), bronchiolitis (n=4), diarrhea/vomiting (n=20), fever (n=27), and minor head injury (n=26) were video recorded. The average length of stay in the ED was 3 hours, with an average of three provider-parent interactions per visit. Interactions ranged in time from less than one minute to 29 minutes, with an average of six minutes per interaction. A total of 34,544 unique utterances were coded across all interactions. The majority of patient visits were first-time visits for the illness presentation (63.2%). Physicians most commonly gave medical information (22.9%) or asked close-ended medical questions (9.4%), whereas nurses most commonly gave orientation instructions (20.9%). Medical trainees were most likely to employ active listening techniques (e.g. back channels, 14.2%). Communication that included post-discharge instructions for parents comprised 8.5% of all utterances. Overall, providers infrequently assessed parental understanding of information (2.0%). Parent satisfaction with the amount of information communicated was generally high (89.6% agreed or strongly agreed). CONCLUSION This is the first discharge communication study to be conducted in a paediatric ED context using video observation methods. Provider-parent communication was predominantly characterized by the exchange of medical information, with little time devoted to adequately preparing parents to care for their child at home. Greater assessment of parental comprehension is needed to ensure that parents understand important instructions and know when to seek further care.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.057
GPT teacher head0.339
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes2
Has abstractyes

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