Redirecting patients from the pediatric emergency department to community locations for care: A qualitative study of healthcare professional and leader perspectives
Bibliographic record
Abstract
OBJECTIVES: Emergency department (ED) to community (ED2C) programs, which redirect patients from the pediatric ED to community healthcare professionals represent a promising strategy to reduce the impact of non-urgent visits on the pediatric ED. Given an ED2C program's potential impact on various care professionals, we completed a qualitative study to explore key informants' attitudes and perceptions of pediatric ED2C programs. METHODS: We conducted one-on-one semi-structured interviews with key informants in British Columbia, Canada. Participants included: pediatric ED staff - triage nurses and physicians; community professionals - pediatricians and family physicians; and health system leaders responsible for pediatric and emergency care in British Columbia. Interviews were recorded, transcribed verbatim, de-identified, and analyzed using reflexive thematic analysis within an interpretive description framework. A visual model was developed to depict key themes in attitudes and perceptions towards pediatric ED2C programs. RESULTS: We interviewed 24 participants: 6 community professionals, 11 pediatric ED professionals, and 7 healthcare leaders. Participants viewed the ED2C program as a valuable solution to address pediatric system strain provided that systemic barriers are addressed, and both emergency and community settings are equipped with adequate training and resources. Participants emphasized the need for clear guidelines on eligibility and operations to build confidence and enhance program effectiveness. CONCLUSIONS: Our findings suggest there is support for ED2C programs as a means to reduce the impact of non-urgent pediatric ED visits and strengthen community-based care. Successful implementation will require coordinated planning, resource investment, and clear operational frameworks.
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 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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".