Co-designing discharge communication interventions for mental health visits to the pediatric emergency department: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Discharge communication is essential to convey information regarding the care provided and follow-up plans after a visit to a hospital emergency department (ED), but it can be lacking for visits for pediatric mental health crises. Our objective was to co-design and conduct usability testing of new discharge communication interventions to improve pediatric mental health discharge communication. METHODS: The study was conducted in two phases using experience-based co-design (EBCD). In phase 1 (Sep 2021 to Jan 2022), five meetings were conducted with a team of six parents and two clinicians to co-design new ED discharge communication interventions for pediatric mental health care. Thematic analysis was used to identify patterns in team discussions and participant feedback related to discharge communication improvement and the Capability, Opportunity, Motivation, Behavior (COM-B) model was used to identify strategies to support the delivery of the new interventions. After meeting five, team members completed the Public and Patient Engagement Evaluation Tool (PPEET) to evaluate the co-design experience. In phase 2 (Apr to Jul 2022), intervention usability and satisfaction were evaluated by a new group of parents, youth aged 16-24 years, ED physicians, and nurses (n = 2 of each). Thematic analysis was used to identify usability issues and a validated 5-point Likert survey was used to evaluate user satisfaction. Evaluation results were used by the co-design team to finalize the interventions and delivery strategies. RESULTS: Two discharge communication interventions were created: a brochure for families and clinicians to use during the ED visit, and a text-messaging system for families after the visit. There was high satisfaction with engagement in phase 1 (overall mean PPEET score, 4.5/5). In phase 2, user satisfaction was high (mean clinician score, 4.4/5; mean caregiver/youth score, 4.1/5) with both interventions. Usability feedback included in the final intervention versions included instructions on intervention use and ensuring the text-messaging system activates within 12-24 h of discharge. CONCLUSIONS: The interventions produced by this co-design initiative have the potential to address gaps in current discharge practices. Future testing is required to evaluate the impact on patients, caregivers, and health care system use after the ED visit.
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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 | 0.057 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".